Design and Analysis of Collaborative Unmanned Surface ...downloads.hindawi.com/journals/jat/2019/1323105.pdf · of vehicles to relieve the burden of providing continuous oversight

Research ArticleDesign and Analysis of Collaborative Unmanned Surface-AerialVehicle Cruise Systems

Man Zhu 12 and Yuan-Qiao Wen134

1 Intelligent Transportation Systems Research Center Wuhan University of Technology 430063 Hubei China2Computer Science Carl-von-Ossietzky University of Oldenburg 26121 Oldenburg Germany3National Engineering Research Center for Water Transport Safety 430063 Hubei China4Hubei Key Laboratory of Inland Shipping Technology 430063 Hubei China

Correspondence should be addressed to Man Zhu manzhuuni-oldenburgde

Received 8 August 2018 Revised 8 December 2018 Accepted 11 December 2018 Published 14 January 2019

Academic Editor Yair Wiseman

Copyright copy 2019 ManZhu andYuan-QiaoWenThis is an open access article distributed under theCreativeCommonsAttributionLicensewhichpermits unrestricteduse distribution and reproduction in anymedium provided the original work is properly cited

With the increasing application of unmanned surface vehicle-unmanned aerial vehicles (USV-UAVs) in maritime supervisionresearch on their deployment and control is becoming vitally importantWe investigate the application of USV-UAVs for synergisticcruising and evaluate the effectiveness of the proposed collaborative model First we build a collaborative model consisting of thecruise vehicles and communication detection and command-and-control networks for theUSV-UAV Second based on an analysisof the problems faced by collaborative USV-UAV systems we establish a model to evaluate the effectiveness of such synergisticcruisesThird we propose a weighting method for each evaluation factor Finally a model consisting of one UAV and four USVs isemployed to validate our synergistic cruise model

1 Introduction

An unmanned surface vehicle (USV) can be used for vari-ous tasks in different application areas such as intelligencesurveillance of coasts port and border security autonomoussearching signals transmission between air and underwatervehicles and submarine protection An unmanned aerialvehicle (UAV) can be utilized in many missions includ-ing search-and-rescue operations aerial surveillance andremote-sensing applications

Two of the purposes of maritime safety administrationare to build a modern supervision platform and to realizeintelligentmaritime supervision based on an electronic patrolsystem In recent years electronic patrol systems have beenemployed under the concept of perception ships in maritimesupervision [1]

Intelligent vehicles unmanned surface vehicle(s) andunmanned aerial vehicle(s) (UAVs and USVs) are widelyused in military and civilian applications Their flexiblemanipulation and mobility enable them to collect surveil-lance reconnaissance and road traffic information [2] Dueto the small size and lighter weight of UAVs and USVs they

can be deployed quickly by small teams near the mission sitewithout substantial physical infrastructure Their relativelylow deploy cost also permits deployment more frequentlyand riskier missions in challenging terrains A single UAVor a single USV can fulfil nearly all aspects of a task suchas both searching a large area efficiently and providing high-fidelity reconnaissance data but some data like imagery isalways difficult for a single UAV or single USV An USV canget up close to objects or targets to provide high-resolutionimagery can be fast-moving and also can cover wide areasquickly above most obstacles or danger zones But USVscannot carry large accurate sensors and execute long run-time missions In contrast an UAV can carry large sensorsand execute long run-time missions but it can only providelow-resolution images of targets at a distance Generally theUAV can fly in an altitude of at least some hundreds ofmeters Due to this different characteristic compared to theUSV the narrow beam demerit of LIDAR based rangefinder[3] would not be a serious obstruction to the UAV Hencea LIDAR based rangefinder could be much more suitablethan the ultrasonic rangefinder [3] to be equipped with theUAV for objects detection Additionally this characteristic of

HindawiJournal of Advanced TransportationVolume 2019 Article ID 1323105 10 pageshttpsdoiorg10115520191323105

2 Journal of Advanced Transportation

the USV also impacts the selection and use of a mechanismto decode information from camera pictures Even thoughRFID is the most reliable method compared to Bluetoothand OCR to conduct the information decoding task [4] itsshort range is a barrier for its application in UAV Thereforeit should be advantageous to use both in a system to leveragethese complementary properties We purpose a collaborativemodel that combines their strengths Through this collabora-tion UAVs and USVs can cruise synergistically and providericher information to an electronic patrol system Such acollaborative operation may even provide more accuratereal-time navigation information for traffic organizationthus ensuring water traffic safety and greatly improving theefficiency of maritime supervision

An USV-UAV collaborative system can accomplish sev-eral tasks such as surveillance and reconnaissance searchand rescue mapping unknown environment and payloadtransportation Such synergetic cruise has many advantagessuch as the ability to handle more complex tasks ability totackle a task with increased robustness through redundancyincreased efficiency through task distribution and reducedcost of operation Therefore collaboration among multiplevehicles should enhance the performance adaptability andflexibility and fault tolerance To measure the ability of UAVsandUSVs to undertake and complete such synergetic cruisesit is important to evaluate both their individual and combinedeffectiveness

In this paper we first build a model of USV-UAV(1n)synergetic cruises and analyze its effectiveness We thendesign an effectiveness evaluation model for such synergeticcruises Finally we propose a method to determine weightfactors for this evaluation based on a cloud model and theDelphimethod Such amodelmay not only collaborate UAVsandUSVs strengths but also providemore accurate real-timenavigation information for synergetic cruise thus ensuringwater traffic safety and greatly improving the efficiencyof maritime supervision environmental protection bettermonitoring coastal protection and ship navigation

The organization of this paper is as follows Section 2presents literature review Adescription of theUSV-UAV(1n)synergetic cruises and its effectiveness issue is formulatedin Section 3 Effectiveness evaluation model for the pro-posed collaborative model is considered in Section 4 Theempowerment system to give the weight of each factor isexplained in Section 5 Example conducted to explain the useof the effectiveness evaluation model is reported in Section 6Finally Section 7 is devoted to conclusion

2 Literature Review

USVs have been widely used for various tasks in differentapplication areas such as intelligence surveillance of coastsport and border security autonomous searching signalstransmission between air and underwater vehicles and sub-marine protection [5 6] UAVs have been utilized extensivelyin many tasks including search and rescue operations aerialsurveillance and sensing applications [7] In recent years thecollaborative unmanned vehicles and their applications havebeen extensively studied

At present many studies of collaborative unmannedvehicles focus on UAVs UGVs and USVs and related tech-nologies ability and mechanism of collaboration Ryan et al[8] implemented a UAV system that performed collaborativesensing tasks under the supervision of a single user and alsoapplied a collaboration algorithm that combined shared andlocal information to minimize the global cost of the missionTo support multiple-UAV operations in tactical scenariosTortonesi et al [9] studied multiple UAV coordination andcommunications in tactical edge networks Jameson et al[10] studied the autonomy and collaboration ability of a teamof vehicles to relieve the burden of providing continuousoversight of UAV operations enabling the effectiveness ofteams of vehicle to be improved Bastianelli et al [11] studiedcollaborative mechanisms between unmanned vehicles Inorder to close the surveillance gaps of the littoral stateChng [12] comprehensive benefit the unmanned platformscontaining UAVs and USVs and manned patrol crafts (PCs)to develop two concept of operations (CONOPS) with thepurpose of detecting two key types of maritime terrorismthreats including large ships (LSs) and small boasts (SBs)Furthermore the analytical models built by incorporatingboth differential equations and probabilistic arguments wasused to assess theCONOPSThe above studies provide a goodreference for studying synergetic cruises and the effectivenessof USV-UAV systems

Collaboration among multiple vehicles enhances the per-formance adaptability and flexibility and fault tolerance [13ndash15]Therefore this paper will focus on collaboration betweenUAV and USV Such collaboration has many advantagessuch as the ability to handle more complex tasks ability totackle a task with increased robustness through redundancyincreased efficiency through task distribution and reducedcost of operation USV-UAV collaborative system can accom-plish several tasks such as surveillance and reconnaissancesearch and rescue mapping unknown environment and pay-load transportation In this paper USV-UAV collaborativesystem is used to cruise for providing information to anelectronic patrol system In addition the effectiveness issueof such collaborative system is another core research part

3 USV-UAV Synergetic Cruisesand Their Effectiveness

31 Synergetic Cruises In practical applications UAVs andUSVs will be subject tomileage constraints Relative toUSVsUAVs are smaller and faster and have a wider detectionrange but can only handle smaller task loads and are lesscapable of carrying large equipment Both UAVs and USVsare limited in terms of the acquisition processing andcontrol of information as well as in their ability to handlecomplex tasks and respond to changes in work environmentTherefore by integrating the advantages of USVs and UAVsa collaborative system can be proposed to overcome tasksthat may be difficult for single UAVs or USVs Such a systemwould exhibit strong advantages such as parallelism androbustness For instance during a USV-UAV collaborativesystem implementing its tasks one of other UAVs willperform the tasks of the failed UAV provided that the

Journal of Advanced Transportation 3

Unmanned Surface Vehicle

UnmannedAerial Vehicle




Platform Platform

Manned Ground Platform

Detection

Collaborativesystem

Control

Figure 1 The model of USV-UAV(1n) synergetic cruises

collaborative system makes appropriate adjustments whichguarantee that the system continues to complete the assignedtasks However for a singleUAV system the reliability of eachUAVmust be extremely high to ensure that the assigned taskswill be completed as scheduled

In this paper the USV-UAV synergetic cruise refers toa cruise model for collecting and processing water trafficinformation through an air-sea collaborative model In thismode theUSV is the habitat platform and information centrefor several UAVs and the detection range has the USV as itscentre and a radius equivalent to the range of the UAVs

Depending on the number of assigned tasks the USV-UAV synergetic cruises may include a single UAV and severalUSVs a single USV and several UAVs several USVs andseveral UAVs or a single USV and a single UAV

The USV-UAV synergetic cruise consists of multiplesubsystems covering communications navigation detectionand command-and-control Each subsystem has its ownlayers of complexity making it difficult to build a universalmathematical model This paper proposes the USV-UAVsynergetic cruisemodel shown inFigure 1Themodel consistsof one USV and n (= 1 2 3) UAVs and includes subsystemsfor command-and-control communication and detection

The command-and-control subsystem is the nerve centreand is responsible for the control of the entire system(including the USV and UAVs) according to instructionsfrom the manned ground platform (see Figure 1)

The communication subsystem is responsible for com-munication and data transfer between the manned groundplatform and the USV-UAV system as well as intrasystemcommunications In this paper the water moving targetdetection and tracking and static target detection are finishedby multiple sensors based on infrared and visual imageswhich are mounted on the UAVs Then the data and video

information are transmitted to the USVs via a wirelesscommunication system After that the USV utilizes high-performance processor to process and integrate the informa-tion received from UAVs as well as through its own sonarradar and environment sensing device which are transferredto the manned ground platform via satellite communicationsystem After analyzing the information the manned groundplatform transfers instructions about further cruise tasksto USV through satellite communication system Finallyeach UAV gets the instructions from USV through wirelesscommunication system to take further actions

The detection subsystem is a sensor network consistingof detection equipment aboard the USV and UAVs and isresponsible for collecting information The command-and-control function of the collaborative system is located on theUSV

In the collaborative system the USV-UAVs send detec-tion information about cruise objectives to the USV TheUSV is responsible for decision-making and informationintegration as well as controlling the UAVs Upon receivingorders the UAVs take the necessary actions

32 Effectiveness Issue The effectiveness of USV-UAV syner-getic cruises directly affects the quality and quantity of infor-mation collected If the cruise is ineffective certain requiredinformation may be subject to intolerable delay deletion oromission Hence to improve the effectiveness of USV-UAVsynergetic cruises we analyze the major influencing factorsand build a series of evaluation models

USV-UAV synergetic cruises can be divided into threechronological stages First the command-and-control stagegathers cruise information and sets out the command-and-control of the entire synergy system Second the cruisestage detects information on the cruise objectives Finally


the synergy stage integrates cognitive and detection syn-ergy throughout the entire cruise Accordingly the overalleffectiveness of USV-UAV synergetic cruises depends on (1)synergy capacity (2) command-and-control capacity and (3)cruise capacity

Synergy capacity plays a key role as it is responsiblefor linking the target detection system with the command-and-control system through the communication networkHence synergy guarantees that detected information fromeach platform can be shared with other platforms almostimmediately and enables effective command of the USV andUAVs to complete the cruise tasks Additionally synergycapacity is influenced by the acquisition distribution andsharing of information

The command-and-control capacity mainly relies onreceiving and sending instructions control ability and robustperformance When cruise objectives are detected the UAVssend detection information to the USV which rapidly pro-cesses and integrates this data The USV comprehensivelytreats this information so that UAVs can carry out furthercruise tasks The above processes involve the command-and-control

The cruise capacity refers to the ability to acquireinformation on cruise objectives and complete cruise tasksThis cruise capacity directly affects the effectiveness andis impacted by changes in environment and detectivityDetectivity is the ability of the detection platform to recognizeand locate cruise objectives within the detection range Envi-ronmental adaptability allows the synergy system to gatherinformation by perceiving recognizing and understandingthe natural environment

4 Effectiveness Evaluation Model forUSV-UAV Synergetic Cruises

We have identified the factors that influence the effectivenessof USV-UAV synergetic cruises systems We can evaluate theoverall effectiveness of the synergy model as

119864 = 119891 (119864119904 119864119888119888 119864119888) = 120596119904119864119904 + 120596119888119888119864119888119888 + 120596119888119864119888 (1)

where 119864119904 119864119888119888 and 119864119888 denote the effectiveness of the synergycapacity command-and-control capacity and cruise capacityrespectively and 120596119904 120596119888119888 and 120596119888 are their respective weightsrespectively

41 Evaluation of Synergy Capacity Good synergy capacitywill ensure that the USV-UAV system performs effectivelyFactors affecting synergy capacity include the capability ofinformation sharing information distribution and informa-tion acquisition Thus the evaluation model for the synergycapacity can be written as

119864119904 = 120596119894119904119864119894119904 + 120596119894119889119864119894119889 + 120596119894119886119864119894119886 (2)

where119864119894119904119864119894119889 and119864119894119886 denote the effectiveness of informationsharing information distribution and information acquisi-tion respectively and 120596119894119904 120596119894119889 and 120596119894119886 are the weights of 119864119894119904119864119894119889 and 119864119894119886 respectively

411 Evaluation of Information Sharing When the maincontrol platform USV receives information from mannedground platforms it will process and share this data across thesynergetic cruises system The whole process of informationsharing is impacted by network reliability The evaluation ofinformation sharing is modelled as follows

119864119894119904 = 120582 119873119894119904sum119899119894=1119873119894119903119894 (3)

where120582 isin (0 1) is the network reliability119873119894119904 is the amount ofshared information from themain controlUSV inunit time 119899is the number of ground platforms that transmit informationto the main control USV in unit time and119873119894119903119894 is the amountof information that the main control USV receives fromground platforms in unit time

412 Evaluation of Information Distribution Informationdistribution reflects the ability of the main USV to transmitthe information it receives to other platforms This ability isheavily influenced by network reliability The evaluation ofinformation distribution is modelled as follows

119864119894119889 = 120582sum119899119894=1119873119894119886119894119873119894119904 (4)

where 120582 isin (0 1) represents network reliability 119899 is thenumber of platforms that receive information from the maincontrol USV in unit time 119873119894119886119894 is the amount of informationreceived by non-main-control platforms in unit time and119873119894119904is the amount of information shared by themain control USVin unit time

413 Evaluation of Information Acquisition The connectionstatus is measured by the digital communication successprobability between all platforms communication systemsThis plays a decisive role in the information acquisition abilityof the systemThe digital communication success probabilityis always calculated using the mistakenly believed rate 119875119890Theevaluation of information acquisition is therefore modelledas

119864119894119886 = 1 minus 119875119890 (5)

42 Evaluation of Command-and-Control Capacity Whilethe synergy capacity is in good condition the command-and-control capacity is the key to ensuring that the USV-UAVsystem runs normally The command-and-control capacityis affected by the reception and sending of instructionscontrol ability and robust performance Therefore the eval-uation of command-and-control capacity can be modelled asfollows

119864119888119888 = 120596119900119903119864119900119903 + 120596119900119905119864119900119905 + 120596119894119888119864119894119888 + 120596119903119887119864119903119887 (6)

where 119864119900119903 119864119900119905 119864119894119888 and 119864119903119887 denote the effectiveness of theinstruction reception and instruction sending control abilityand robust performance respectively and 120596119900119903 120596119900119905 120596119894119888 and120596119903119887 are their respective weights respectively


421 Evaluation of Instruction Reception Instructions aresent by the ground platform The proportion of theseinstructions received by the main control USV determinesthe instruction reception ability The evaluation model istherefore

119864119900119903 = 119873119903119873119892119904 (7)

where 119873119903 is the number of instructions received by themain control USV in unit time and 119873119892119904 is the number ofinstructions transmitted from manned ground platform inunit time

422 Evaluation of Instruction Sending After receiving in-structions from the ground command-and-control platformthe main control USV may need to send commands to otherplatforms within the collaborative system The evaluation ofinstruction sending is modelled as follows

119864119900119905 = sum119898119894=1119873119894119873119906119904V (8)

where 119898 stands for the number of platforms receivinginstructions in unit time 119873119894 is the number of instructionsreceived by each platform inunit time and119873119906119904V is the numberof instructions sent by the main control USV in unit time

423 Evaluation of Control Ability In this paper the controlability is represented by the efficiencywithwhich instructionsare followed by the cruise systemThe command-and-controlsystem in USV-UAV synergetic cruises has high timelinessrequirements and must make decisions within a certain timeBeyond this allotted time period the system effectively losesits command-and-control capacity According to the processof command-and-control this time period is determinedby five components The control efficiency is expressed asfollows

119864119894119888 = exp(minus07119879119904119906119898119879119898119886119909 ) (9)

where119879119898119886119909 is the allowed time of command-and-control and119879119904119906119898 is actual total time of command-and-control

119879119904119906119898 = 119879119906 + 119879119888 + 119879119889 + 119879119903 + 119879119886 (10)

where 119879119906 is the time from discovering a target to reportingto the main control USV 119879119888 is the time in which the maincontrol USV fuses and disposes information 119879119889 is the timefor the main control USV distributing cruise tasks 119879119886 is thetime in which the main control USV monitors the cruise andmakes adjustment

424 Evaluation of Robust Performance The robustness ofperformance is measured by the probability that the USV-UAV synergetic cruises will fail during cruise tasks Thisprobability is related to the mean time between failuresof the USV and UAVs and the time required to performtasks The evaluation of robust performance is modelled asfollows

119864119903119887 = 120575 (1 minus 120578119906119904V) exp(minus119905119906119904V119879119906119904V )

+ (1 minus 120575) (1 minus 120578119906119886V) exp(minus119905119906119886V119879119906119886V )

(11)

where 120575 isin (0 1) is a constant 119905119906119904V and 119905119906119886V denote the timetaken by the USV and UAVs to perform tasks respectivelyand 119879119906119904V and 119879119906119886V denote the mean time between failuresof the USV and UAVs respectively 120578119906119904V is the vulnerabilitycoefficient of the USV and 120578119906119886V is the average vulnerabilitycoefficient of UAVs

43 Evaluation of Cruise Capacity The cruise capacity ex-presses the ability of the USV-UAV collaborative system toperform and implement cruise tasks The two main factorsinfluencing this capacity are environmental adaptability anddetectivity Therefore the evaluation of cruise capacity can bemodelled as follows

119864119888 = 120596119890119886119864119890119886 + 120596119894119889119864119894119889 (12)

where 119864119890119886 and 119864119894119889 refer to the effectiveness of environmentadaptability and detectivity respectively 120596119890119886 and 120596119894119889 are theirrespective weights respectively

431 Evaluation of Environmental Adaptability Environ-mental adaptability is the basis whereby the USV-UAV syn-ergetic cruises system can realize intelligent cruising This isaffected by many factors and is therefore difficult to measureWang [16] analyzed environmental adaptability from theperspective of the variety of environments time to adapt andthe scope of adapting environment Because the USV-UAVsynergetic cruises system will be applied at sea or on inlandwaters there is little variety in the environment Thus theevaluation of environmental adaptability is modelled withoutconsidering different environments

119864119890119886 = 120582max119899119894=1 119879119894119879119898119886119909 + (1 minus 120582) sum

119899119894=1 119860 119894119860 (13)

where 120582 isin (0 1) is a constant 119899 is the total number of allUSV and UAVs of the system 119879119894 is the time for UAV or USVto adapt to environment 119879119898119886119909 is the maximum allowed time119860 119894 is the scope that USV or UAV can perceive the state ofenvironment and 119860 is the total scope needed to be cruised

432 Evaluation of Detectivity Detectivity can be measuredusing the detection probability If the detection probability ofeach platform in the USV-UAV synergetic cruises system is119875(119880) the detectivity can be evaluated as follows

119864119894119889 = 1 minus [119875 (119880)]119899 (14)

where 119899 is the total number of all USV and UAVs of thesystem


5 Empowerment Based on a Cloud Modeland the Delphi Method

In this study we use a cloud model [17ndash21] and the Delphimethod [22] to determine the weight of each evaluationfactor First we formulate a weight cloud model for eachevaluation factorThenwenormalize the expectation of theseweight models to give the weight of each evaluation factor

51 Delphi Method The advantages of Delphi method [22]lie in its convenience feasibility certain scientificity andcertain practicality The characteristics of Delphi method areanonymity iteration and feedback

Anonymity Participants (mostly refer to experts) are ap-proached by mail or computer

Iteration There are several rounds The first round can beinventory in which participants are asked for events to beforecast or parameters to be estimated In subsequent roundsparticipants are asked to give quantitative estimates aboutdates of future events or values of unknown parameters Thenumber of rounds is fixed in advance or determined accord-ing to a criterion of consensus in the group of participants orstability in individual judgments

Feedback The results of an eventual first inventory roundare clustered and sent back to all participants In the firstestimation round participants give their quantitative esti-mates Before the second and subsequent estimate roundsthe results of the whole group on the previous round are fedback in a statistical format to all participants On the secondand subsequent estimation rounds participantsmaking judg-ments that deviate from the first-round group score accordingto a fixed criterion are asked to give arguments for theirdeviating estimates Before the third and subsequent estimaterounds these arguments are along with the statistical resultsfed back to all participants

The steps of application of Delphi method are as followsfirstly one or two panels are recruited The experts on thepanels are not needed to be large in number Secondly eachexpert is sent an initial questionnaire inquiring his or heropinions Thirdly the completed questionnaires are returnedto the monitoring team that summarise the response andcirculate them back to the panel members accompanied bya new reply form Fourthly in the first iteration the panelmembers compare their responses with those of their peercolleagues The iteration is repeated until the replied viewsconverge Fifthly when there is no further progress towardsconsensus the procedure is stopped

52 Cloud Model A cloud generator is a vital part of anycloud model as it produces cloud droplets according tonumerical characteristics which are calculated according tothe cloud distribution As it is universally applicable we usea normal cloud algorithm and an improved reverse cloudalgorithm

The normal cloud algorithm proceeds as followsInput (119864119909 119864119899119867119890) the required number of cloud droplets

119901

Output 119889119903119900119901(119909119894 119910119894) 119894 = 1 2 3 119901(1) Generate a normal random number 1198641015840119899 with expecta-

tion 119864119909 and standard deviation 119867119890(2) Generate a normal random number 119909119894 with expecta-

tion 119864119899 and standard deviation 1198641015840119899 in which 119909119894 is acloud droplet belonging to the domain space

(3) 120583119894 = exp[minus(119909119894 minus119864119909)22(1198641015840119899)2] in which 120583119894 is the mem-bership degree of 119909119894 with respect to some qualitativeconcept

Repeat steps 123 until 119901 cloud droplets have been gener-ated

The improved reverse algorithm can be described asfollows

Input 119909119894(119894 = 1 2 3 119901)Output (119864119909 119864119899 119867119890)

119909 = 1119901119901

sum119894=1

119909119894

119861 = 1119901119901

sum119894=1

1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816

1198782 = 1119901 minus 1

119901

sum119894=1

(119909119894 minus 119909)2

119864119909 = 119909119864119899 = radic120587

2 times 119861

and 119867119890 = radic1198782 minus 1198642119899

(15)

53 Empowerment Method We determine the weight of eachevaluation factor as follows

First select 119902 experts that are familiar with and fullyunderstand themeaning of the evaluation factorsWe assumethat each evaluation factor is influenced by 119898 subfactors1198801198941 1198801198942 119880119894119898

Second if the 119902 experts consider evaluation factor119880119894119895(119895 =1 2 119898) and give a score set of 1198811 1198812 119881119902 then we usethe improved reverse cloud generator to obtain the numericalweight characteristics (119864119909119894119895 119864119899119894119895 119867119890119894119895) for 119880119894119895

Third based on (119864119909119894119895 119864119899119894119895 119867119890119894119895) the cloud atlas of 119880119894119895 isobtained from the forward cloud generator

Fourth the condensation of cloud droplets in the cloudatlas is examined If the distribution of cloud droplets pro-duces amist (ie the cohesion of cloud droplets is poor) thenthe experts have not provided unified evaluation commentsIn this case we must reconsolidate the evaluation comments

The above operation is repeated until the expertsrsquo eval-uation comments are unified to give a cohesive cloud atlaswhich is the final weight cloud of the evaluation factor

Finally the above steps are repeated for all 119898 evaluationfactors

The weight of 119880119894119895 is then 120596119894119895 = 119864119909119894119895sum119898119895=1 119864119909119894119895


Table 1 The relative parameters and effectiveness of each evaluation factor

Evaluation factor Parameters Effectiveness

Information sharing (IS) 120582 = 08 the percent of information that comes from USVshared with UAV is 90 07200

Information distribution(ID)

120582 = 08 the percent of information that UAVs receive fromsharing information from USV is 95 07600

Information acquisition(IA) Mistakenly believed rate is 5 09500

Instruction reception (OR) The percent of instruction that comes from groundcommand-and-control centre received by USV is about 98 09800

Instruction sending (OT) All of the 3 UAVs can receive instruction coming from USV andthe acceptance percent is 90 09000

Control (IC) The allowed time for command-and-control is fifth as long asthe actual total time 08694

Robust performance (RB) 120578119906sV = 5 120578119906119886V = 10 119905119906119904V119879119906119904V = 0001 119905119906119886V119879119906119886V = 0002 120575 =06 09287

Environment adaptability(EA) 120582 = 08max119899119894=1119879119894119879119898119886119909 = 09 sum119899119894=1 119860 119894119860 = 00001 07200

Detectivity (ID) 119875(119880) = 075 119899 = 4 09961

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(a)

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(b)

Figure 2 The weight of ldquoinformation sharingrdquo (a) represents the first cloud weight and (b) indicates the final best cloud weight

6 Numerical Example and Analysis

In this section a synergetic cruises system consisting of oneUSV and three UAVs that is USV-UAV(13) is chosen toverify the effectiveness evaluation model proposed Withoutloss of generality we assume that the three UAVs belong tothe same type and have the same performance The weatherconditions of cruise area are good The instruments andequipment of the collaborative system have stable perfor-mance and can be normally used In addition the network isstable and bandwidth is enough to meet the use The relativeparameters of each evaluation factor are listed in Table 1 Theeffectiveness of the collaborative system is calculated after theweight of each evaluation factor is determined

According to the empowerment based on cloud modeland Delphi method the weight of each evaluation factor ofUSV-UAV synergetic cruises can be ensured In order to wellunderstand the application of the empowerment method theevaluation factor named ldquoinformation sharingrdquo is taken as anexample

Firstly there are ten experts scoring for ldquoinformationsharingrdquo marked as (3 5 5 7 5 3 7 5 3 5) We canget numerical characteristics value which is (4800 1353605879) Then we get the cloud atlas shown in Figure 2(a)based on forward cloud generator From Figure 2(a) wecan see that the dispersion of cloud droplets is relativelylarge and the cloud atlas is shown as mist So we shouldcollate expertsrsquo scores for feedback to experts and prepare


Table 2 The weights of the evaluation factors of USV-UAV synergetic cruises

Upper level Evaluation factor Subscript Weight

OverallSynergy capacity S 02352

Command-and-control capacity CC 03208Cruise capacity C 04442

Synergy capacityInformation sharing IS 02949

Information distribution ID 03462Information acquisition IA 03590

Command-and-control capacity

Instruction reception OR 02619Instruction sending OT 02619

Control ability IC 02937Robust performance RB 01825

Cruise capacity Environment adaptability EA 04625Detectivity ID 05375

next round of expertsrsquo scoring Repeat above operation untilunifying experts cognition and get final numerical charac-teristics signed as (46000 08021 02602) of ldquoinformationsharingrdquo whose cloud atlas is shown in Figure 2(b) So dothe remaining three evaluation factors The final weight ofldquoinformation sharingrdquo is 02949 through normalizing theabove three expectations Theweights of all evaluation factorsof USV-UAV synergetic cruises are listed in Table 2

From the weight of each evaluation factor of USV-UAVsynergetic cruises we can see that the weight of ldquocruisecapacityrdquo is 04442 which is the biggest compared with theother two evaluation factors in the same layer index and hasthe most prominent effect on the effectiveness of USV-UAVsynergetic cruises ldquoInformation acquisitionrdquo whose weightis 03590 is a much important factor to ldquosynergy capac-ityrdquo In terms of ldquocommand-and-control capacityrdquo ldquocontrolabilityrdquo makes major contribution to it It is ldquodetectivityrdquothat produces more influence on ldquocruise capacityrdquo comparedwith ldquoenvironment adaptabilityrdquo Therefore ldquocruise capacityrdquoldquoinformation acquisitionrdquo ldquocommand-and-control capacityrdquoand ldquocontrol abilityrdquo should be improved so that the totaleffectiveness of USV-UAV collaborative system could beenhanced

According to the effectiveness evaluationmodel proposedin this paper the effectiveness values of synergy capac-ity command-and-control capacity and cruise capacity are08165 09172 and 08684 respectively At the same time thetotal effectiveness value of USV-UAV system is 08720 Thusthe USV-UAV system is in good effectiveness which can wellfinish given cruise tasks and provide a wealth of informationfor electronic patrol system

7 Conclusion

This paper has considered a USV-UAV collaborative systemWe have focused on the application of synergistic cruiseof USV-UAV system to electronic patrol systems and haveestablished a collaborative model and a method for evalu-ating its effectiveness Furthermore we have quantitativelyevaluated and verified the proposed system using a numericalexample

Both unmanned surface vehicle (USV) and unmannedaerial vehicle (UAV) are valuable in ocean and coastalmanagement USV can be used for intelligence surveillanceof coasts port and border security autonomous searchingsignals transmission between air and underwater vehiclesand submarine protection UAV can be used for search andrescue operations aerial surveillance and sensing applica-tions They are often used separately and independentlyRemote sensing (or sea search and rescue or armed attacksof maritime pirates) operations of UAV are often limitedby the viability and sea conditions For remote sensing infoggy sea conditions it will be better to have USV andUAV collaboratively For example in Bohai Sea in the springseason the fog is very thick and it is impossible for anypractical operation of UAV alone because an UAV alonecannot carry large heavy sensors and carry out long run-time tasks But it is possible for USV-UAV(1n) collaborativesystem to carry out some tasks In this system theUSVcarrieslarge sensors such as sonar radar and environment sensingdevice and executes long run-time tasks The UAV gets upclose to objects or targets to provide high-resolution imageryis fast-moving and also can cover wide areas quickly abovemost obstacles or danger zones in the case of fog on the basisof command-and-control of the USV

In a USV-UAV(1n) system the USV can be used tobe served as a mobile control station of the UAVs Theadvantages of this command-and-control system are thatUSV can be used as energy supply and landing platformfor UAVs to enhance implementation and adaptability ofUAVs It can share databases and communicate with the on-shore (fixed) command-and-control station It can monitoranalyze and process the real-time information on spot Itcan load long-distance call and analyze information quicklyand combine with the information collected by UAVs andinstructions from ground control station to do situationanalysis and forecasting

Future potential applications of collaborative operationsinclude surveillance and reconnaissance search and rescuemapping unknown environment and payload transporta-tion Surveillance and reconnaissance are quoted as exampleA collaborative system can conduct real-time monitoring


to sand-mining operations in the inland waters so that theoverharvesting and illegal operations can be avoided In thecoastal waters a collaborative system is able to supervisefishing areas and fishing vessels operations to avoid the harmcaused by overfishing to marine ecological balance

The major challenges of collaboration between USV andUAVs are to build a collaborative system to develop col-laborative mechanisms and to evaluate collaborative perfor-mance This paper has attempted to address these challengesby analyzing USV-UAV system and its subsystems proposingdefinition of USV-UAV synergetic cruise building USV-UAVsystem and establishing effectiveness evaluation model forUSV-UAV collaborative system

Our research on synergetic cruises and their effectivenesshas laid the foundations for further study on the applicationof USV-UAV systems in electronic patrol scenarios Howeverchanging application requirements and environments meansthat our USV-UAV synergetic cruises model must be contin-ually improved

Data Availability

The simulated data by Matlab used to support the findings ofthis study are included within the article

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The work described in this presentation was supported bythe National Natural Science Foundation of China (NSFC)through Grant No 51579204 and Double First-Rate Projectof WUT the National Key RampD Program of China throughGrant No 2018YFC1407405 and the Ministry of Science andCulture of Lower Saxony for funding the Graduate School ofSafe Automation of Maritime Systems (SAMS)

References

[1] YHaoY-Y Li S-XChenC-Y Jiang andGHu ldquoInformationattributes of electronic patrol system and its application inmaritime supervisionrdquo Navigation of China vol 37 no 1 pp11ndash15 2014

[2] R-J Yan S Pang H-B Sun and Y-J Pang ldquoDevelopmentand missions of unmanned surface vehiclerdquo Journal of MarineScience and Application vol 9 no 4 pp 451ndash457 2010

[3] Y Wiseman ldquoAncillary Ultrasonic Rangefinder for Autono-mous Vehiclesrdquo International Journal of Security and Its Appli-cations vol 10 no 5 pp 49ndash58 2018

[4] Y Wiseman ldquoVehicle Identification by OCR RFID and Blue-tooth for Toll Roadsrdquo International Journal of Control andAutomation vol 11 no 9 pp 67ndash76 2018

[5] T Phanthong T Maki T Ura T Sakamaki and P AiyarakldquoApplication of A algorithm for real-time path re-planning ofan unmanned surface vehicle avoiding underwater obstaclesrdquo

Journal of Marine Science and Application vol 13 no 1 pp 105ndash116 2014

[6] G Tuna B Nefzi and G Conte ldquoUnmanned aerial vehicle-aided communications system for disaster recoveryrdquo Journal ofNetwork and Computer Applications vol 41 no 1 pp 27ndash362014

[7] K Y Chee and Z W Zhong ldquoControl navigation and collisionavoidance for an unmanned aerial vehiclerdquo Sensors and Actua-tors A Physical vol 190 pp 66ndash76 2013

[8] A Ryan J Tisdale M Godwin et al ldquoDecentralized controlof unmanned aerial vehicle collaborative sensing missionsrdquo inAmerican Control Conference ACC07 pp 4672ndash4677 IEEE2007

[9] M Tortonesi C Stefanelli E Benvegnu K Ford N Suriand M Linderman ldquoMultiple-UAV coordination and com-munications in tactical edge networksrdquo IEEE CommunicationsMagazine vol 50 no 10 pp 48ndash55 2012

[10] S Jameson J Franke R Szczerba and S Stockdale ldquoCollabora-tive autonomy for mannedunmanned teams inrdquo in Proceedingsof the Annual Forum Proceedings-American Helicopter Societyvol 61 p 1673 American Helicopter Society 2005

[11] G Bastianelli D Salamon A Schisano and A IacobaccildquoAgent-based simulation of collaborative unmanned satellitevehiclesrdquo in Proceedings of the 2012 IEEE 1st AESS EuropeanConference on Satellite Telecommunications ESTEL 2012 ItalyOctober 2012

[12] K C ChngDetermining a cost-effective mix of uav-usv-mannedplatforms to achieve a desired level of surveillance in a congestedstrait [PhD thesis] Naval Postgraduate SchoolMonterey CalifUSA 2007

[13] L Consolini F Morbidi D Prattichizzo and M TosquesldquoStabilization of a hierarchical formation of unicycle robotswith velocity and curvature constraintsrdquo IEEE Transactions onRobotics vol 25 no 5 pp 1176ndash1184 2009

[14] Z Ren Z Jiang Z Gao and R Skjetne ldquoActive tugger line forcecontrol for single blade installationrdquoWindEnergy vol 21 no 12pp 1344ndash1358 2018

[15] D V Dimarogonas P Tsiotras and K J Kyriakopoulos ldquoLead-er-follower cooperative attitude control of multiple rigid bod-iesrdquo Systems amp Control Letters vol 58 no 6 pp 429ndash435 2009

[16] Y Wang and J Liu ldquoEvaluation methods for the autonomy ofunmanned systemsrdquo Chinese Science Bulletin vol 57 no 26 pp3409ndash3418 2012

[17] D Y Li and L Y Liu ldquoStudy on the universality of the normalcloudmodelrdquo Engineering Science vol 6 no 8 pp 28ndash34 2004

[18] M Zhu A Hahn and Y Wen ldquoIdentification-based controllerdesign using cloudmodel for course-keeping of ships in wavesrdquoEngineering Applications of Artificial Intelligence vol 75 pp 22ndash35 2018

[19] D Li C Liu andWGan ldquoAnew cognitivemodel cloudmodelrdquoInternational Journal of Intelligent Systems vol 24 no 3 pp357ndash375 2009

[20] R-X Guo J-B Xia L Zhang and Y Qian ldquoResearch onmultiple attribute evaluation method based on cloud modelrdquoin Proceedings of the 2010 IEEE International Conference onAdvanced Computer Control ICACC 2010 pp 103ndash106 March2010

[21] M Zhu Y Wen C Zhou and C Xiao ldquoComprehensiveEvaluation Cloud Model for Ship Navigation AdaptabilityrdquoTransNav the International Journal on Marine Navigation andSafety of Sea Transportation vol 8 no 3 pp 331ndash336 2014


[22] C Zhilong ldquoUsing delphi method and AHP for the research onkindergarten teachersrsquo comprehensive quality evaluation indexweightrdquo Journal of Chemical and Pharmaceutical Research vol6 no 4 pp 6ndash13 2014

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the USV also impacts the selection and use of a mechanismto decode information from camera pictures Even thoughRFID is the most reliable method compared to Bluetoothand OCR to conduct the information decoding task [4] itsshort range is a barrier for its application in UAV Thereforeit should be advantageous to use both in a system to leveragethese complementary properties We purpose a collaborativemodel that combines their strengths Through this collabora-tion UAVs and USVs can cruise synergistically and providericher information to an electronic patrol system Such acollaborative operation may even provide more accuratereal-time navigation information for traffic organizationthus ensuring water traffic safety and greatly improving theefficiency of maritime supervision

An USV-UAV collaborative system can accomplish sev-eral tasks such as surveillance and reconnaissance searchand rescue mapping unknown environment and payloadtransportation Such synergetic cruise has many advantagessuch as the ability to handle more complex tasks ability totackle a task with increased robustness through redundancyincreased efficiency through task distribution and reducedcost of operation Therefore collaboration among multiplevehicles should enhance the performance adaptability andflexibility and fault tolerance To measure the ability of UAVsandUSVs to undertake and complete such synergetic cruisesit is important to evaluate both their individual and combinedeffectiveness

In this paper we first build a model of USV-UAV(1n)synergetic cruises and analyze its effectiveness We thendesign an effectiveness evaluation model for such synergeticcruises Finally we propose a method to determine weightfactors for this evaluation based on a cloud model and theDelphimethod Such amodelmay not only collaborate UAVsandUSVs strengths but also providemore accurate real-timenavigation information for synergetic cruise thus ensuringwater traffic safety and greatly improving the efficiencyof maritime supervision environmental protection bettermonitoring coastal protection and ship navigation

The organization of this paper is as follows Section 2presents literature review Adescription of theUSV-UAV(1n)synergetic cruises and its effectiveness issue is formulatedin Section 3 Effectiveness evaluation model for the pro-posed collaborative model is considered in Section 4 Theempowerment system to give the weight of each factor isexplained in Section 5 Example conducted to explain the useof the effectiveness evaluation model is reported in Section 6Finally Section 7 is devoted to conclusion

2 Literature Review

USVs have been widely used for various tasks in differentapplication areas such as intelligence surveillance of coastsport and border security autonomous searching signalstransmission between air and underwater vehicles and sub-marine protection [5 6] UAVs have been utilized extensivelyin many tasks including search and rescue operations aerialsurveillance and sensing applications [7] In recent years thecollaborative unmanned vehicles and their applications havebeen extensively studied

At present many studies of collaborative unmannedvehicles focus on UAVs UGVs and USVs and related tech-nologies ability and mechanism of collaboration Ryan et al[8] implemented a UAV system that performed collaborativesensing tasks under the supervision of a single user and alsoapplied a collaboration algorithm that combined shared andlocal information to minimize the global cost of the missionTo support multiple-UAV operations in tactical scenariosTortonesi et al [9] studied multiple UAV coordination andcommunications in tactical edge networks Jameson et al[10] studied the autonomy and collaboration ability of a teamof vehicles to relieve the burden of providing continuousoversight of UAV operations enabling the effectiveness ofteams of vehicle to be improved Bastianelli et al [11] studiedcollaborative mechanisms between unmanned vehicles Inorder to close the surveillance gaps of the littoral stateChng [12] comprehensive benefit the unmanned platformscontaining UAVs and USVs and manned patrol crafts (PCs)to develop two concept of operations (CONOPS) with thepurpose of detecting two key types of maritime terrorismthreats including large ships (LSs) and small boasts (SBs)Furthermore the analytical models built by incorporatingboth differential equations and probabilistic arguments wasused to assess theCONOPSThe above studies provide a goodreference for studying synergetic cruises and the effectivenessof USV-UAV systems

Collaboration among multiple vehicles enhances the per-formance adaptability and flexibility and fault tolerance [13ndash15]Therefore this paper will focus on collaboration betweenUAV and USV Such collaboration has many advantagessuch as the ability to handle more complex tasks ability totackle a task with increased robustness through redundancyincreased efficiency through task distribution and reducedcost of operation USV-UAV collaborative system can accom-plish several tasks such as surveillance and reconnaissancesearch and rescue mapping unknown environment and pay-load transportation In this paper USV-UAV collaborativesystem is used to cruise for providing information to anelectronic patrol system In addition the effectiveness issueof such collaborative system is another core research part

3 USV-UAV Synergetic Cruisesand Their Effectiveness

31 Synergetic Cruises In practical applications UAVs andUSVs will be subject tomileage constraints Relative toUSVsUAVs are smaller and faster and have a wider detectionrange but can only handle smaller task loads and are lesscapable of carrying large equipment Both UAVs and USVsare limited in terms of the acquisition processing andcontrol of information as well as in their ability to handlecomplex tasks and respond to changes in work environmentTherefore by integrating the advantages of USVs and UAVsa collaborative system can be proposed to overcome tasksthat may be difficult for single UAVs or USVs Such a systemwould exhibit strong advantages such as parallelism androbustness For instance during a USV-UAV collaborativesystem implementing its tasks one of other UAVs willperform the tasks of the failed UAV provided that the







Platform Platform


Detection

Collaborativesystem

Control




















119864 = 119891 (119864119904 119864119888119888 119864119888) = 120596119904119864119904 + 120596119888119888119864119888119888 + 120596119888119864119888 (1)



119864119904 = 120596119894119904119864119894119904 + 120596119894119889119864119894119889 + 120596119894119886119864119894119886 (2)



119864119894119904 = 120582 119873119894119904sum119899119894=1119873119894119903119894 (3)



119864119894119889 = 120582sum119899119894=1119873119894119886119894119873119894119904 (4)



119864119894119886 = 1 minus 119875119890 (5)


119864119888119888 = 120596119900119903119864119900119903 + 120596119900119905119864119900119905 + 120596119894119888119864119894119888 + 120596119903119887119864119903119887 (6)




119864119900119903 = 119873119903119873119892119904 (7)



119864119900119905 = sum119898119894=1119873119894119873119906119904V (8)



119864119894119888 = exp(minus07119879119904119906119898119879119898119886119909 ) (9)


119879119904119906119898 = 119879119906 + 119879119888 + 119879119889 + 119879119903 + 119879119886 (10)





(11)



119864119888 = 120596119890119886119864119890119886 + 120596119894119889119864119894119889 (12)



119864119890119886 = 120582max119899119894=1 119879119894119879119898119886119909 + (1 minus 120582) sum

119899119894=1 119860 119894119860 (13)



119864119894119889 = 1 minus [119875 (119880)]119899 (14)












119901







Input 119909119894(119894 = 1 2 3 119901)Output (119864119909 119864119899 119867119890)

119909 = 1119901119901

sum119894=1

119909119894

119861 = 1119901119901

sum119894=1

1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816

1198782 = 1119901 minus 1

119901

sum119894=1

(119909119894 minus 119909)2

119864119909 = 119909119864119899 = radic120587

2 times 119861

and 119867119890 = radic1198782 minus 1198642119899

(15)





















Detectivity (ID) 119875(119880) = 075 119899 = 4 09961

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(a)

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(b)




















7 Conclusion









Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia








Platform Platform


Detection

Collaborativesystem

Control




















119864 = 119891 (119864119904 119864119888119888 119864119888) = 120596119904119864119904 + 120596119888119888119864119888119888 + 120596119888119864119888 (1)



119864119904 = 120596119894119904119864119894119904 + 120596119894119889119864119894119889 + 120596119894119886119864119894119886 (2)



119864119894119904 = 120582 119873119894119904sum119899119894=1119873119894119903119894 (3)



119864119894119889 = 120582sum119899119894=1119873119894119886119894119873119894119904 (4)



119864119894119886 = 1 minus 119875119890 (5)


119864119888119888 = 120596119900119903119864119900119903 + 120596119900119905119864119900119905 + 120596119894119888119864119894119888 + 120596119903119887119864119903119887 (6)




119864119900119903 = 119873119903119873119892119904 (7)



119864119900119905 = sum119898119894=1119873119894119873119906119904V (8)



119864119894119888 = exp(minus07119879119904119906119898119879119898119886119909 ) (9)


119879119904119906119898 = 119879119906 + 119879119888 + 119879119889 + 119879119903 + 119879119886 (10)





(11)



119864119888 = 120596119890119886119864119890119886 + 120596119894119889119864119894119889 (12)



119864119890119886 = 120582max119899119894=1 119879119894119879119898119886119909 + (1 minus 120582) sum

119899119894=1 119860 119894119860 (13)



119864119894119889 = 1 minus [119875 (119880)]119899 (14)












119901







Input 119909119894(119894 = 1 2 3 119901)Output (119864119909 119864119899 119867119890)

119909 = 1119901119901

sum119894=1

119909119894

119861 = 1119901119901

sum119894=1

1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816

1198782 = 1119901 minus 1

119901

sum119894=1

(119909119894 minus 119909)2

119864119909 = 119909119864119899 = radic120587

2 times 119861

and 119867119890 = radic1198782 minus 1198642119899

(15)





















Detectivity (ID) 119875(119880) = 075 119899 = 4 09961

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(a)

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(b)




















7 Conclusion









Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia









119864 = 119891 (119864119904 119864119888119888 119864119888) = 120596119904119864119904 + 120596119888119888119864119888119888 + 120596119888119864119888 (1)



119864119904 = 120596119894119904119864119894119904 + 120596119894119889119864119894119889 + 120596119894119886119864119894119886 (2)



119864119894119904 = 120582 119873119894119904sum119899119894=1119873119894119903119894 (3)



119864119894119889 = 120582sum119899119894=1119873119894119886119894119873119894119904 (4)



119864119894119886 = 1 minus 119875119890 (5)


119864119888119888 = 120596119900119903119864119900119903 + 120596119900119905119864119900119905 + 120596119894119888119864119894119888 + 120596119903119887119864119903119887 (6)




119864119900119903 = 119873119903119873119892119904 (7)



119864119900119905 = sum119898119894=1119873119894119873119906119904V (8)



119864119894119888 = exp(minus07119879119904119906119898119879119898119886119909 ) (9)


119879119904119906119898 = 119879119906 + 119879119888 + 119879119889 + 119879119903 + 119879119886 (10)





(11)



119864119888 = 120596119890119886119864119890119886 + 120596119894119889119864119894119889 (12)



119864119890119886 = 120582max119899119894=1 119879119894119879119898119886119909 + (1 minus 120582) sum

119899119894=1 119860 119894119860 (13)



119864119894119889 = 1 minus [119875 (119880)]119899 (14)












119901







Input 119909119894(119894 = 1 2 3 119901)Output (119864119909 119864119899 119867119890)

119909 = 1119901119901

sum119894=1

119909119894

119861 = 1119901119901

sum119894=1

1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816

1198782 = 1119901 minus 1

119901

sum119894=1

(119909119894 minus 119909)2

119864119909 = 119909119864119899 = radic120587

2 times 119861

and 119867119890 = radic1198782 minus 1198642119899

(15)





















Detectivity (ID) 119875(119880) = 075 119899 = 4 09961

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(a)

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(b)




















7 Conclusion









Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




119864119900119903 = 119873119903119873119892119904 (7)



119864119900119905 = sum119898119894=1119873119894119873119906119904V (8)



119864119894119888 = exp(minus07119879119904119906119898119879119898119886119909 ) (9)


119879119904119906119898 = 119879119906 + 119879119888 + 119879119889 + 119879119903 + 119879119886 (10)





(11)



119864119888 = 120596119890119886119864119890119886 + 120596119894119889119864119894119889 (12)



119864119890119886 = 120582max119899119894=1 119879119894119879119898119886119909 + (1 minus 120582) sum

119899119894=1 119860 119894119860 (13)



119864119894119889 = 1 minus [119875 (119880)]119899 (14)












119901







Input 119909119894(119894 = 1 2 3 119901)Output (119864119909 119864119899 119867119890)

119909 = 1119901119901

sum119894=1

119909119894

119861 = 1119901119901

sum119894=1

1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816

1198782 = 1119901 minus 1

119901

sum119894=1

(119909119894 minus 119909)2

119864119909 = 119909119864119899 = radic120587

2 times 119861

and 119867119890 = radic1198782 minus 1198642119899

(15)





















Detectivity (ID) 119875(119880) = 075 119899 = 4 09961

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(a)

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(b)




















7 Conclusion









Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia












119901







Input 119909119894(119894 = 1 2 3 119901)Output (119864119909 119864119899 119867119890)

119909 = 1119901119901

sum119894=1

119909119894

119861 = 1119901119901

sum119894=1

1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816

1198782 = 1119901 minus 1

119901

sum119894=1

(119909119894 minus 119909)2

119864119909 = 119909119864119899 = radic120587

2 times 119861

and 119867119890 = radic1198782 minus 1198642119899

(15)





















Detectivity (ID) 119875(119880) = 075 119899 = 4 09961

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(a)

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(b)




















7 Conclusion









Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia














Detectivity (ID) 119875(119880) = 075 119899 = 4 09961

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(a)

0

01

02

03

04

05

06

07

08

09

1

0 1 2 3 4 5 6 7 8 9 10

(b)




















7 Conclusion









Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia
















7 Conclusion









Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






Data Availability




Acknowledgments


References



























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


Documents

Design and Analysis of Collaborative Unmanned Surface ...downloads.hindawi.com/journals/jat/2019/1323105.pdf · of vehicles to relieve the burden of providing continuous oversight