Upload
jemimah-lester
View
220
Download
0
Embed Size (px)
DESCRIPTION
IECON 2005, Raleigh, North Carolina, November Introduction (I) Infrared barriers on railway applications Goals: To detect the presence of obstacles avoiding false alarms To know the obstacle position inside the area of interest Special environments conditions Adverse weather Low SNR System keys Codification of Information: Mutually Orthogonal Complementary Sets of Sequences –MOCSS- Geometrical distribution of emitters and receivers
Citation preview
Dedicated Smart IR Barrier for Obstacle Detection in Railways
J. Jesús García, Cristina Losada, Felipe Espinosa, Jesús Ureña, Álvaro Hernández, Manuel Mazo, Carlos de Marziani, Ana Jiménez,
Emilio Bueno, Fernando Álvarez,
Electronics Department.
University of Alcalá. Spain.IECON’05
IECON 2005, Raleigh, North Carolina, November 6-10 2005 2
Index
Introduction Sensorial system Emission codification False alarms Obstacle location Conclusions
IECON 2005, Raleigh, North Carolina, November 6-10 2005 3
Introduction (I)
Infrared barriers on railway applications Goals:
To detect the presence of obstacles avoiding false alarms To know the obstacle position inside the area of interest
Special environments conditions Adverse weather Low SNR
System keys Codification of Information:
Mutually Orthogonal Complementary Sets of Sequences –MOCSS- Geometrical distribution of emitters and receivers
IECON 2005, Raleigh, North Carolina, November 6-10 2005 4
Introduction (II)
High speed lines: overpasses and tunnels
Overpass Tunnel
Standard rail lines: level crossings
CRITICAL POINTS FOR OBSTACLES ON RAILWAYS
IECON 2005, Raleigh, North Carolina, November 6-10 2005 5
Sensorial System (I)
Structure of the Smart IR barrier
Reception System
Emission System Control System
Alarms
Other sensorial systems
Weather conditions
Data Fusion
Vital zone
Non Vital zone
Non Vital zone
IECON 2005, Raleigh, North Carolina, November 6-10 2005 6
Sensorial System (II)
Requirements
Detection of obstacles without false alarms
Discrimination, at least, between vital area and non vital one
Minimum obstacle size: 0.5x0.5x0.5 m (rail regulations)
Distance between emitters: 25 cm (also between receivers)
Distance between emitter and receiver barriers: >14 m
IECON 2005, Raleigh, North Carolina, November 6-10 2005 7
Sensorial System (III)
Classical solution with IR barrier
Emitter and receiver are aligned in the axial axis. This allows to detect the presence of obstacles between both elements
Impossible to know if the obstacle is located or not in the railway vital area
IECON 2005, Raleigh, North Carolina, November 6-10 2005 8
Sensorial System (IV)
Our proposal: Every receiver processes the information from three emiters (multi-emission) Specific geometrical distribution for the infrared barrier
IECON 2005, Raleigh, North Carolina, November 6-10 2005 9
Index
Abstract Sensorial system Emission codification False alarms Obstacle location Conclusions
IECON 2005, Raleigh, North Carolina, November 6-10 2005 10
Emission Codification (I)
PREVIOUS NOTES
The emission is carried out in a continuous way . The obstacle appearence generates a lack of the reception.
Every receiver detects the radiation from three emiters. How are they distinguished ?
Mutually Orthogonal Complementary Sets of Sequences (MO CSS) are used in order to:
Identify the emissions, and Avoid emitters interferences.
IECON 2005, Raleigh, North Carolina, November 6-10 2005 11
Emission codification (II)
Mutually Orthogonal Complementary Sets of Sequences
4 Sets are generated (number of sets is power of 2 to be MO), but only three are used.
Every set includes 4 complementary binary sequences {-1,+1}, {a, b, c, d} of length L,
Given a set i {ai, bi, ci, di}, if it is CSS then meets : The addition of their auto-correlation function is ideal ([k])
Given 2 sets, if they are MO then meets They both have the same length The addition of their cross-correlation function is zero
IECON 2005, Raleigh, North Carolina, November 6-10 2005 12
Emission codification (III)
Detector output for every emission. (L=256, SNR=-6dB)
a b c d1 1 1 1
Em itte r 2
Em itte r 3
Em itte r 4
Re c e ive r
Em itte r 1
a b c d2 2 2 2
a b c d3 3 3 3
a b c d4 4 4 4
Sync hro n iza tio n
Every emitter i transmits the set {aij, bij, cij, dij} being j=1…L, continously.To emit every symbol we use interleaving.
Every receiver detects a periodic signal with period 4L and a peak 4L.
j
jii LjkLkzoutputDetector )4(4_
0 512 1024 1536 2048 2560 3072 3584 4096-10
010
256 length sequences. SNR = -6dBs
Rec
eive
dsi
gnal
0 512 1024 1536 2048 2560 3072 3584 4096
0500
1000
Set
1
0 512 1024 1536 2048 2560 3072 3584 4096
0500
1000
Set
2
0 512 1024 1536 2048 2560 3072 3584 4096
0500
1000
Set
3
0 512 1024 1536 2048 2560 3072 3584 4096
0500
1000
Set
4
IECON 2005, Raleigh, North Carolina, November 6-10 2005 13
Emission codification (IV)
Emission detection in a practical case (FPGA)
Emitted signal
Peak detection
T
T
TT
1) Ch 1: 5 Volt 5 ms 2) Ch 2: 5 Volt 5 ms
Without obstacle
T
T
TT
1) Ch 1: 5 Volt 5 ms 2) Ch 2: 5 Volt 5 ms
With obstacle
4L
IECON 2005, Raleigh, North Carolina, November 6-10 2005 14
Index
Abstract Sensorial system Emission codification False alarms Obstacle location Conclusions
IECON 2005, Raleigh, North Carolina, November 6-10 2005 15
False alarms (I)
False alarm Alarm:
the receiver does not detect emissions during a predefined time (temporary lack of information)
False alarm: the control system generates an alarm but there is not
obstacle
Examples of false alarms Solar radiation: photodiode saturation Atmospheric attenuation (fog, rain, dust, …..)
IECON 2005, Raleigh, North Carolina, November 6-10 2005 16
False alarms (II)
Atmospheric attenuation (dB), due to meteorology, it can be calculated in function of visibility V(Km) and link range R(Km):
RV
dBLq
atm
55017)(
Visibility V Weather condition q Expected attenuation
V>50km Very clear 1.6 0.19 dB/km
6km<V<50km Clear 1.3 1.82-0.48 dB/km
1km<V<6km Haze /snow /light rain 0.585·V1/3 13.2-1.82 dB/km
0.5km<V<1km Light fog /snow / heavy rain 0.585·V1/3 27.82-13.2 dB/km
V<0.5km Thick fog 0.585·V1/3 >27.82 dB/km
IECON 2005, Raleigh, North Carolina, November 6-10 2005 17
False alarms (III)
How to avoid false alarms
Due to atmospheric attenuation, the correlator output degrades.
To avoid false alarms, an adaptive threshold for the peak detection is proposed, The atmospheric degradation is estimated by polynomial interpolation of degree 1, The estimator output is used to adapt dynamically the threshold.
PeakD etecto r
R eceiver j
O u tdoo r C hann el
z [k] J
Em itte r 1{a ,b ,c ,d }1 1 1 1
Em itte r 2{a ,b ,c ,d }2 2 2 2
Em itte r 3{a ,b ,c ,d }3 3 3 3
Em itte r 4{a ,b ,c ,d }4 4 4 4
A tm o sp h e ric attenu atio nSo lar in terferences
C orrelato rr[k] +
[k]{a ,b ,c ,d }j j j j
Estim atedO utp ut
z [k] J
IECON 2005, Raleigh, North Carolina, November 6-10 2005 18
False alarms (IV)
False alarms discrimination results
0 500 1000 1500 2000 -1000
0 1000
Interpolating Polynomial of degree 1 with SNR = 0dBs Window size 40 samples
Very Clear Light fog Thick fog Haze Very clear
Correlation output
Prediction Threshold
0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1 Obstacle detection with dynamic threshold
0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1 Obstacle detection with fixed threshold
True Obstacles
Obstacle detection
Incorrect obstacle detection
Dynamic threshold evaluation in different weather conditions.
0 100 200 300 400 500 600 700
0
50
100
150
200Low solar radiation High solar radiation
0 100 200 300 400 500 600 700
0
0.2
0.4
0.6
0.8
1
Dynamic Threshold
0 100 200 300 400 500 600 700
0
0.2
0.4
0.6
0.8
1
Fixed Threshold
Dynamic threshold evaluation with different relative levels of sunlight
IECON 2005, Raleigh, North Carolina, November 6-10 2005 19
Index
Abstract Sensorial system Emission codification False alarms Obstacle location Conclusions
IECON 2005, Raleigh, North Carolina, November 6-10 2005 20
Obstacle location (I)
Once the obstacle is detected, its location is possible according to the sensor geometrical distribution
3 boolean functions allows one to recognize 3 different zones
0 0.5 1 1.5 2 2.5 0 2 4 6 8
10 12 14
Track 1
Track 2
Zone 1
Zone 2
Zone 3
IECON 2005, Raleigh, North Carolina, November 6-10 2005 21
Obstacle location (II)
Detection in the track area Detection in the non vital area
If the obstacle is bigger than 0.5 m, at least 6 beams are not received
IECON 2005, Raleigh, North Carolina, November 6-10 2005 22
Conclusions
A smart system for obstacle detection on railways is proposed, based on IR barriers.
The system detects the obstacle, and locates it inside the region of interest (track or non vital area).
The codification technique based on MO CSS allows to identify the emissions and mitigate interferences among emitters.
Typical false alarms are analysed, and a solution is proposed to avoid them using a dynamic adaptive threshold.
Future work: The integration of filtering algorithms (KF, min-max, etc) to reduce the
effect of noisy sources.
ACKNOWLEDGEMENTS
MCYT: SILPAR and PARMEI projects
UAH: ISUAP project
Thank you very much!!
J. Jesús García, Cristina Losada, Felipe Espinosa, Jesús Ureña, Álvaro Hernández, Manuel Mazo, Carlos de Marziani, Ana Jiménez, Emilio
Bueno, Fernando Álvarez,
IECON’05