Improving the Energy Consumption in Mobile Phones by Filtering Noisy

GPS Fixes with Modified Kalman Filters

Isaac Taylor and Miguel LabradorCenter for Urban Transportation Research Department of Computer Science & Engineering

Location Aware State Machine

• Integrated in TRACIT– A mobile application that collects a user’s travel information via

GPS and provides individual feedback [1].

• Determines if valid and useful GPS data is being obtained. • Adjusts the frequency of GPS calculations depending on a

user’s movement [1].• Frequencies used: 4, 8, 16, 64, 150, 256

State0

State1

Staten – 1

Staten

Move towards state[0] upon successful condition evaluation, or jump to state[0]

Move towards state[n] upon failed condition evaluation

Location Recalculation

Interval = 4 sec.

Location Recalculation

Interval = 8 sec.

Location Recalculation

Interval = 5 min.

Location Recalculation

Interval = 15 min.

State Machine Problems

Energy ConsumptionIncorrect data may erroneously increase the number of GPS calculationsunnecessarily, thus wasting the cellular phone’s energy.

Application PerformanceIncorrect data may erroneously decrease the number of GPS calculations, thus losing important tracking information for the application.

Incorrect Behavior

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Robust Kalman Filter (RKF)

• Developed by Ting, J 2007• Main idea

– Give the static noise variable R a dynamic weighting factor

• Changes to the Kalman Filter:

For more information see [5]

Advantages 1. No increase in Computation Complexity2. No use of heuristics

Disadvantages 1. Tuning of the filter is required

a. Values for a, b, Q, and R are needed2. Some knowledge of outliers are necessary

Q = R = .0001 (recommended by Ting)a = b = 1 (recommended by Ting)

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• Developed by Rutan, S in 1991• Main idea

– Compute a new error value every time a new point is encountered

• Changes to the Kalman Filter (taken from the RKF):

For more information see [3]

Adaptive Kalman Filter (AKF)

Advantages 1. Little tuning of the filter needed (Q)2. Ease of implementation

Disadvantages 1. Could increase computations if window is large2. No way to adjust the filter if needed3. Untested with GPS

Q = .0001 (recommended by Ting)

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Adaptive Robust Kalman Filter (ARKF)

• Simply a combination of the above two filters• R is calculated (AKF) and then weighted (RKF)

Advantages 1. Any outliers potentially overlooked by one of the other filter may be caught here

Disadvantages 1. Increased Complexity

a. R is now dynamically computed, and weighted

Outlier Test

The dataset contains:• 394 GPS Fixes• 4 Outliers (3 stacked)

WhyProve that each filter removes outliers in GPS data

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TRACIT Test

• Why– To Determine if the integration of the three modified Kalman filters with

the Location Aware State Machine: • Reduces energy consumption • Leaves the amount of valid travel data unchanged

• How– Four phones, one with each filter and one without a filter, are carried for

30 days to produce 30 tests.– After data is recorded, each point is labeled as stationary or traveling,

based on a travel log kept for a particular day.

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TRACIT Test MetricsThree Metrics1. Incorrect State Point (ISP)

• Uses the time difference between two consecutive GPS fixes.• This time difference is used to determine which state the state machine is in at a given

time• A difference is counted as a ISP if the state machine is in an incorrect state

• In an awake state when the user is stationary or vise versa2. Stationary Fix Counts

• Total fixes taken while stationary – Used to track energy consumption3. Traveling Fix Counts

• Total fixes taken while traveling – Used to check the amount of valid travel data received

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Normal RKF AKF ARKF

Average 11.60% 14.48% 7.61% 9.95%

% Difference -2.88% 3.98% 1.65%

Incorrect State Point(ISP)

• Uses the time difference between two consecutive GPS fixes to determine what state the Location Aware State Machine is currently in.

AKF increases the state machine performance by about 4 %

•A point is an ISP if:• it has a difference of less than 5 seconds and

is labeled “stationary”• it has a difference greater than 8 seconds and

is labeled “traveling”

• Values for 6 and 7 are not accounted for becausethe state of the Location Aware State Machine is difficult to determine at these values due tothe behavior of GPS.

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Stationary Fix Count

• Total number of Stationary Fixes generated in a test/day

AKF reduces the total number of Stationary fixes by 17.4 %

•Each point is compared to a travel log for a given day

•All points generated when the travel log states “stationary” are counted

Normal RKF AKF ARKF

Average 315.33 389.33 260.33 298.07

% Difference -23.487% 17.44% 5.45%

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Traveling Fix Count

• Total number of Stationary Fixes generated in a test/day

AKF leaves the traveling fix count unchanged

•Each point is compared to a travel log for a given day

•All points generated when the travel log states “traveling” are counted

•The closer a filter’s Traveling Fix Count is to the Normal's, the smaller the change is in application performance

Normal RKF AKF ARKF

Average 691.57 705.17 691.70 683.53

% Difference -1.97% -0.02% 1.16%

Conclusions

• AFK shows the largest decrease (17.5% ) in Stationary fixes while not affecting the application performance

References1. Barbeau, S., Perez, R, Labrador, M. A., Perez, A., Winters, P., Georggi, N.. LAISYC A

Location-Ware Framework to Support Intelligent Real-Time Applications for GPS-Enabled Mobile Phones. IEEE Pervasive Computing (to appear 2010).

2. Kalman, R.. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering, 82:35–45, 1960.

3. Rutan, S.. Adaptive Kalman Filtering. Analytical Chemistry, 63:1103A1109A, 1991.4. Sharkley, J.. Coding for Life–Battery Life, That Is.

http://dl.google.com/io/2009/. Accessed Jan 20115. Ting, J., Theodorou, E., Schaal, S.. A Kalman Filter for Robust Outlier Detection.

Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2007, pages 1514–1519, 2007.

6. Welch, G., Bishop, G.. An Introduction to the Kalman Filter. ACM SIGGRAPH International Conference on Computer Graphics and Interactive Techniques, Chapel Hill, NC, USA, 1995. University of North Carolina at Chapel Hill.