principles of radar target tracking jay bhalodi, jeff cao, lily healey, wendy lin, tuling ma, zara...
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Principles of Principles of Radar Target TrackingRadar Target Tracking
Jay Bhalodi, Jeff Cao, Lily Healey, Jay Bhalodi, Jeff Cao, Lily Healey, Wendy Lin, Tuling Ma, Zara Mannan, Wendy Lin, Tuling Ma, Zara Mannan,
Brandon Millman, Zachary Purdy, Brandon Millman, Zachary Purdy, Divya Sharma, Mimi XuDivya Sharma, Mimi Xu
The Corporations
CheetahTrack
Jay Bhalodi, Lily Healey, Wendy Lin, Tuling Ma, Mimi Xu
TRACJeffrey Cao, Zara Mannan, Brandon Millman, Zachary Purdy, Divya Sharma,
Government Agent
Consultant
Randy Heuer
Zachary Vogel
Problem and SolutionProblem and Solution
Solution: Kalman Filter
Updates to better approximate noise
Problem: Noise
Inaccuracies in measurement data
Account for noise to better predict
Kalman Filter: Background
Derived by R.E. Kalman
Published A New Approach to Linear Filtering and Prediction Problems in the Journal of Basic Engineering in 1960
Kalman Filter used extensively in fields of navigation and tracking
Kalman Filter Model
kkk
kkk
rHxy
qxx
1
=
The foundation of the Kalman filter lies in its model of both the target’s movement and the actual measurement of the position.
Kalman Theory
The Kalman Filter is a two-step algorithm :
First the algorithm “predicts” the target’s next expected location
Then update predictions based on new measurements
PREDICT UPDATE
Predict Step
1
Q 1
Predicts using transition matrix and current velocity value
Advances state covariance matrix for update step
Update Step
1
11
RHHPHPK Tkk
Tkkk
11ˆˆˆ kkkkkkkk xHyKxx
1 kkkkk PHKIPCalculatesKalman Gain Matrix
Updates position matrix based on weighting factor and residual
Recalculates state covariance matrix for predict step
ImplementationImplementation
Modular - easy to modify
Different class for filter and each matrix
Java - Efficient due to object-oriented nature
ImplementationImplementation
JAMA Matrix LibraryJava Libraries
JAMA Matrix Library
Vector Class
National Institute of Standards and Technology (NIST)
Residuals- difference between our results and real data
Adaptations
Adapted filter to different challenging environments:
Polar Conversions
Two Radars
Collision Avoidance
Maneuvering Targets
Intercepting Targets
r
α
θ
Polar Conversions
sin
cos2
ry
rx
Real life applications-Range and Bearing
Transformed coordinate system
Updating the R Matrix
222222 sincos rrx
222222 cossin rry
2222 2sin2
1rrxy
yxy
xyxR
22
22
Error of range and bearing
not along the xy plane
Multiple Radars
Added update method to recalculate state transition (Φ) matrix
Two changes:
multiple data-input sources
variable time
Implementation:
Tagged data to later reconcile to single reference frame
Collision Avoidance
Some Changes:
Track two targets
Within 12 mi, predict paths
Within 1 mi, prompt for evasive action
Collision Avoidance (cont.)
Sequence of Steps:
Run filter for each target
Check distance each iterationCheck distance each iteration
If less than 12 miles:
Solve for timeSolve for time
Predict if they will come within 1 mi of each other
(40)
01)()22()( 222 yxvvtvvt xyyx
Maneuvering Targets
The Change:
Target no longer follows one linear path and may maneuver
The Steps:
•Detect•Count•Reset
Res
idua
ls
Intercepting Targets
βTarget
Point of Interception
Interceptor
τ
α
N
D
B A
γ
E
• Use Law of Sines to find α
• and β can be found using
BA
sinsin
sin)(sin 1
B
A
I
T
I
T
v
v
tv
tv
B
A
yx
yx
TT
TTTITI
vvD
vvyyxx
,
,,(cos 1
B
A
Intercepting Targets
22 )()( ITIT yyxxD
)(cos 1
D
yy IT
)(cos 1
D
yy IT
βTarget
Point of Interception
Interceptor
τ
α
N
D
B A
γ
Further Applications
Real Time Radar Tracking
Variable Altitudes
Acceleration
Conclusion
• Exposure to and successful implementation of Kalman Filter
• Many adaptations for our tracking system
• Overall, successful and effective
THANK YOU! Randy Heuer and Zachary VogelRandy Heuer and Zachary Vogel Dr. MiyamotoDr. Miyamoto Paul and CounselorsPaul and Counselors Course and Lab TeachersCourse and Lab Teachers
Thank you
Jewish Communal Fund
John and Laura Overdeck
NJGSS Alumnae and Parents, 1984 - 2008
Novartis
Schering-Plough Foundation
The Dorr Foundation
The Edward W. and Stella C. Van Houten Memorial Fund
The Jennifer A. Chalsty Foundation
Any Questions?Any Questions?
ReferencesReferences[1] Blackman SS. 1986. Multiple-Target Tracking with Radar Applications. Artech House, Inc.
[2] Atwood B. 2003. Covariance and GLAST. <http://www-glast.slac.stanford.edu/software/AnaGroup/WBA072003-Covariance.pdf>. Accessed 2008 July 21.
[3] [IEEE] Institute of Electrical and Electronics Engineers. 2003 Jan 23. Rudolf E. Kalman, 1930-. IEEE History Center. <http://www.ieee.org/web/aboutus/history_center/biography/kalman.html>. Accessed 2008 July 21.
[4] Kalman, R. E. 1960. A New Approach to Linear Filtering and Prediction Problems. ASME Journal of Basic Engineering 1960 March.