Maneuvering target tracking method via modifying the interacting multiple model (IMM) and the interacting acceleration compensation (IAC) algorithms
First Claim
1. A method of maneuvering target tracking via the modified IMM algorithm having the innovation covariance SJ(k)=HJ(k)PJ(k|k−
- 1)HJ(k)T+RJ(k), wherein SJ(k) being the innovation covariance, HJ(k) being the measurement matrix, PJ(k|k−
1) being the state prediction covariance and RJ(k) being the measurement noise covariance, comprising the steps of;
(a) in each model j (j=1,2), introducing the scaling factors ξ
J to the innovation covariance, SJ(k)=HJ(k)PJ(k|k−
1)HJ(k)T+RJ(k), as SJ(k)=HJ(k)[FJ(k)PJ(k−
1|k−
1)FJ(k)T+ξ
J×
QJ(k)]HJ(k)T+RJ(k), where ξ
J are the scaling factors that will scale up the process noise covariance QJ(k) of each model, F(k) is the system matrix. (b) setting an initialized threshold to the maneuver detector, ψ
=VJ(k)SJ(k)−
1VJ(k)T, a little larger with respect to the present scenario, wherein VJ(k) is the innovation.
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Abstract
The present invention relates to the field of target tracking and more generally to a method employing improved algorithms, which achieve excellent tracking performance for a high-g maneuvering target. The two-model Interacting Multiple Model algorithm and the Interacting Acceleration Compensation algorithm will be modified by introducing adaptive factors through the detection of the normalized innovation squared which is chi-square probability distributed. The implementation results show that the modified algorithms can handle the target sudden maneuver better and are more accurate than the original algorithms.
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Citations
2 Claims
-
1. A method of maneuvering target tracking via the modified IMM algorithm having the innovation covariance SJ(k)=HJ(k)PJ(k|k−
- 1)HJ(k)T+RJ(k), wherein SJ(k) being the innovation covariance, HJ(k) being the measurement matrix, PJ(k|k−
1) being the state prediction covariance and RJ(k) being the measurement noise covariance, comprising the steps of;
(a) in each model j (j=1,2), introducing the scaling factors ξ
J to the innovation covariance, SJ(k)=HJ(k)PJ(k|k−
1)HJ(k)T+RJ(k), as SJ(k)=HJ(k)[FJ(k)PJ(k−
1|k−
1)FJ(k)T+ξ
J×
QJ(k)]HJ(k)T+RJ(k), where ξ
J are the scaling factors that will scale up the process noise covariance QJ(k) of each model, F(k) is the system matrix.(b) setting an initialized threshold to the maneuver detector, ψ
=VJ(k)SJ(k)−
1VJ(k)T, a little larger with respect to the present scenario, wherein VJ(k) is the innovation. - View Dependent Claims (2)
- 1)HJ(k)T+RJ(k), wherein SJ(k) being the innovation covariance, HJ(k) being the measurement matrix, PJ(k|k−
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