Multi Sensor Data Fusion In Target Tracking

1. Multi Sensor Data Fusion In Target
Tracking
S.Vasuhi
Guided by:
Dr.V.Vaidehi
Prof. & Head,
Dept. of Electronics Engineering
Madras Institute of Technology,Chennai

2. Objective
• To perform data fusion within the sampling
interval, the fusion information has to be sent
to the tracking center for accurate future
prediction.
• Both computational load and communication
load are to be minimized.
• If conventional algorithms are not suitable
due to computation overhead, Fuzzy logic
fusion and its hardware realization will be
investigated

3. Areas of Use
Military applications:
–Automated target recognition (e.g. smart
weapons)
–Guidance for autonomous vehicles
–Remote sensing
–Battlefield surveillance
Nonmilitary applications:
–Robotic navigation
–Airport Surveillance
–Medical diagnosis

4. Elements of Multiple Target Tracking
• Detection
• Recognition
• Identification
• Tracking
• Change detection
• Decision making

5. Need of multiple sensors
• Performance of target search and tracking over an
wider area.
• Early initiation of new tracks
• Track continuity
• Higher detection opportunities in overlapping areas
• System reconfiguration in case of failure of one or
more sensors
• Improved performance

6. Elements of Multiple Target Tracking
Correlation
Filtering and
prediction
Display output
Track
Termination
Track initiation
Successful
Correlation
New Track
Old track
yes
No
Yes
No
Yes
No
Plots from
Data extractor
Predicted track

7. Gating and Data Association
• Gating: Technique for eliminating unlike
observation to track- formed around the predicted
track position
• Association: Correlate one set of observations with
another set of observations-able to produce a set of
tracks for a target object
Types :
• Nearest Neighbor
• Joint Probabilistic Data Association (JPDA)
• Lagrangian Relaxation Artificial Neural
Networks(ANNs)
• Fuzzy logic

8. Position and Attribute Estimation
• Is the process of taking the associated measurements and
calculating target states
Types :
• Kalman Filter
• Multiple model Algorithms
• Multiple resolutional Filtering
• Particle Filtering
• Artificial Intelligent Approaches
i) Rule – Based
ii) Artificial Neural Networks (ANNs)

9. Identification
• Classifies the object that the measurements
originates from
Types :
• Bayesian Interference
• Dempster-Shafer (D-S) Rule of Combination
• Artificial Neural Networks (ANNs)
• Expert systems
• Voting and Summing Approaches
• Distributed Classification

10. What is Data Fusion?
• Process of combining information from multiple
sensors and/or data sources
• Composed of several sensors (Primary and
secondary Radar, Passive sensors, Acoustic
Sensors, Image sensors, etc.,) that provide
data (Detections, Attributes ,Tracks) of every
element in the covered environment
• The fused data is presented to controllers after
being merged at the fusion center.

11. Need of data fusion
• Increased confidence
• Reduced ambiguity
• Improved detection
• Increased robustness
• Enhanced spatial and temporal coverage
• Decreased cost

12. Fusion Techniques
Fusion types:
• Measurement fusion: Directly fuses the sensor
measurements to obtain a combined measurement
• Track to Track Fusion: The filtered outputs of the data
are suitably combined in the fusion center to obtain an
improved state estimates
• Fusion Techniques are drawn from:
i) Pattern Recognition
ii) Statistical estimation
iii) Artificial Intelligence

13. Fusion Architectures
• Centralized Architecture – Both Tracking and data
fusion are done at the centralized data fusion center
–Accurate tracking, Do both multiple target tracking and data fusion
• Distributed Architecture – Maintain central and local
tracks (for each sensor)-The distributed processing of measures
that allow to adapt the functions specifically to each type of sensor, the capacity
to calibrate sensor
• Hybrid Architecture – Combination of the
Centralized and Distributed Architecture – High accuracy
systematic error robustness and distribution of computational load

14. Centralized Architecture
Sensor1
Sensor2
Sensor N
Coordinate Transformation
Coordinate Transformation
Coordinate Transformation
Gating
Central Tracks
Management
Association Filtering

15. Distributed Architecture
Sensor1 Coordinate Transformation Gating
Local Track
Management
Association Filtering
Sensor2 Coordinate Transformation Gating
Local Track
Management
Association Filtering
Sensor N Coordinate Transformation Gating
Local Track
Management
Association Filtering
Fusion of
Local
Tracks

16. Hybrid Architecture
Sensor 1 Coordinate Transformation Gating
Local Track
Management
Association Filtering
Sensor 2 Coordinate Transformation Gating
Local Track
Management
Association Filtering
Sensor N Coordinate Transformation Gating
Local Track
Management
Association Filtering
Assign
Measures to
Central Tracks
Central Track
Filtering
Central Tracks
Management

17. Summary
Local processor Global processor
Fixed Coordinate
Polar Coordinate
Kalman Filtering and
Prediction
Track Association
(Different approach)
Estimated track
Estimated track
From different
Local processor
Track to track
association
is performed
Tracks corresponds
to same target are
fused

18. Implementation of weighted least square method
ASSUMPTIONS
• Two non-maneuvering targets in a clutter free
environment.
• Zero mean, white Gaussian process noise

19. SENSOR 1:
• Scan rate: 0.5 sec
• Scans : 150
• Location in LLA
– Latitude –10.502
– Longitude –80.1
– Altitude –10
• Standard deviation of errors in
– Range – 50 m
– Azimuth – 0.005759 radians
– Elevation – 0.0171radians

20. SENSOR 2:
• Scan rate: 1 sec
• Scans : 75
• Location in LLA
– Latitude – 10.50
– Longitude – 80.1
– Altitude – 10
• Standard deviation of errors in
– Range – 100 m
– Azimuth – 0.005759 radians
- Elevation – 0.0171radians

21. FUSION CENTER
• Location in LLA
– Latitude – 10.501
– Longitude – 80.1
– Altitude – 10
• Distributed environment

22. Zi
Compute
X k/k-1 , P (k/k-1)
Compute
X k/k-1 , P (k/k-1)
Compute State
Estimate Xi(k/k)
Compute State
Estimate Xj(k/k)
Gain Ki
Computer Estimation
Error
ei(k) = X k/k-1 – Xk/k
Computer Estimation
Error
ej(k) = X k/k-1 – Xk/k
Compute
Covariance Pi(k/k)
Compute
Covariance Pj(k/k)
Xj( k/k) , Pj(k/k)
Fusion Center
Xi( k/k) , Pi(k/k)
Kj
Zj
FLOWCHART

23. Compute
Covariance of fused estimates
ij (k) = xi(k/k) – xj(k/k)
Fuse the estimates using fusion equation
Different
Target
Compute
Covariance of ij (k)
Compute D
NO
YES
Is D<D
STOP
Sensor i Sensor j
Xi( k/k) , Pi(k/k) Xj( k/k) , Pj(k/k

24. WLS EQUATIONS
C=[HTP-1H] -1
K=CHTP-1
Y = Kx
• P- Prediction error covariances from individual
sensors
• H- Measurement matrix
• C- Error co variances calculated at the fusion centre
• K - Weights given to the estimates at fusion centre

25. RESULTS AND CONCLUSIONS
Parameter Weighted Least
Square
Weight Decision
approach
Linear
Estimation
fusion
Average
Rms
error in
metres
Target 1 108.76 100.47 107.73
Target 2 94.36 94.22 94.85
Computational Complexity High (due to matrix
inversion)
Low High (due to
matrix
inversion
Communication overhead per
scan
90 12 90

26. Track 1 in ECEF Co ordinates : True ( Red) , Sensor 1(Green),Sensor2(Blue)

28. Fused Track 1 in ECEF Co ordinates : True(Blue) , WLS(Red)

29. Track 2 in ECEF Co ordinates : True(Blue) , WLS(Red)

30. Average rms error for track1: WLS(Red), LEF(Blue),WD(Green)

31. Average rms error for track2: WLS(Red), LEF(Blue),WD(Green)

32. rms error in x direction for track1: WLS(Red), LEF(Blue),WD(Green)

33. rms error in y direction for track1: WLS(Red), LEF(Blue),WD(Green)

34. rms error in z direction for track1: WLS(Red), LEF(Blue),WD(Green)

35. rms error in x direction for track2: WLS(Red), LEF(Blue),WD(Green)

36. rms error in y direction for track2: WLS(Red), LEF(Blue),WD(Green)

37. rms error in z direction for track 2 WLS(Red), LEF(Blue),WD(Green)

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