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Published in:
2011 | OriginalPaper | Chapter
Quadrature Kalman Probability Hypothesis Density Filter for Multi-Target Tracking
Authors : Pu Zhang, Hongwei Li, Yuan Huang
Published in: Informatics in Control, Automation and Robotics
Publisher: Springer Berlin Heidelberg
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A new Quadrature Kalman-Probability Hypothesis Density filter is proposed for nonlinear system model multi-target tracking. The algorithm estimates first-order statistical moment of posterior multi-target states with Probability Hypothesis Density (PHD) Filter, and updating targets’ states from the recursion of Quadrature Kalman. Quadrature Kalman filter uses statistical linear regression method through a set of Gaussian-Hermite quadrature points to linearize nonlinear functions, Jacobian matrix solving is unnecessary and the linearized error is reduced. Simulation results prove that influence on the algorithm by non-linear system model is effectively decreased, the estimating accuracy of targets’ number and states is advanced, and the tracking exactness is improved.