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Date of Publication:2013-01-01

Journal:控制与决策

Issue:1

Page Number:36-42,48

ISSN No.:1001-0920

Abstract:Aiming at the nonlinear system model, a central difference Kalman-probability hypothesis density filter is proposed to track multiple targets. Multi-target tracking is fulfilled by deriving polynomial approximations with Stirling interpolation formulas, estimating first-order statistical moment of posterior multi-target states with central difference Kalman filter and Gaussian mixture probability hypothesis density filter, and extracting targets' states from the recursion of probability hypothesis density. The advantage of proposed filter is mainly that Jacobian matrix solving is unnecessary and second-order Taylor expansion accuracy can be ensured. Simulation results show that the robustness of the algorithm is enhanced, and the estimating accuracy of the number and states of the targets are improved.

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