Maneuvering target tracking method via modifying the interacting multiple model (IMM) and the interacting acceleration compensation (IAC) algorithms

• US 20030200065A1
• Filed: 04/22/2002
• Published: 10/23/2003
• Est. Priority Date: 04/20/2001
• Status: Active Grant

• Current Assignee: Singapore Technologies Aerospace Limited (Government of Singapore)
• Original Assignee: Singapore Technologies Aerospace Limited (Government of Singapore)
• Inventors: Lam, Chian Poh, Li, Luo Wen
• Granted Patent: US 7,079,991 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544

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.

Claims

1. A method of maneuvering target tracking via the modified IMM algorithm having the innovation covariance S^J(k)=H^J(k)P^J(k|k−

1)H^J(k)^T+R^J(k), wherein S^J(k) being the innovation covariance, H^J(k) being the measurement matrix, P^J(k|k−

1) being the state prediction covariance and R^J(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, S^J(k)=H^J(k)P^J(k|k−

1)H^J(k)^T+R^{J(k), as SJ}(k)=H^J(k)[F^J(k)P^J(k−

1|k−

1)F^J(k)^T+ξ

^J×

Q^J(k)]H^J(k)^T+R^J(k), where ξ

^J are the scaling factors that will scale up the process noise covariance Q^J(k) of each model, F(k) is the system matrix.

(b) setting an initialized threshold to the maneuver detector, ψ

=V^J(k)S^J(k)<sup>−

1</sup>V^J(k)T, a little larger with respect to the present scenario, wherein V^J(k) is the innovation.

2. A method of maneuvering targets tracking via the modified IAC algorithm, comprising the steps of:

(a) in bias-free model, introducing the scaling factor ξ

to the innovation covariance, S(k)=H(k)P(k|k−

1)H(k)^T+R(k), as S(k)=H(k)[F(k)P(k−

1|k−

1)F(k)^T+ξ

×

Q(k)]H(k)^T+R(k) where ξ

is the scaling factor that will scale up the bias-free model process noise covariance Q(k).

(b) in bias model, introducing the scaling factors ζ

to the innovation covariance, Σ

(k|k−

1)=Σ

(k−

1|k−

1)+G^b(k−

1)Q^b(k−

1)G^b(k−

1)^T, as Σ

(k|k−

1)=Σ

(k−

1|k−

1)+ζ

×

G^b(k−

1)Q^b(k−

1)G^b(k−

1)^T, where Σ

(k|k−

1) is the bias model innovation covariance and ζ

is the scaling factor that will scale up the bias model process noise covariance Q^b(k).

(c) setting an initialized threshold to the maneuver detector, ψ

=V^J(k)S^J(k)<sup>−

1</sup>V^J(k)^T, as set forth in claim 1.