A parameter adaptive improved algorithm based on the GIW-PHD filter

The random matrix approach to extended objects tracking provides efficient estimation of both the states and the extensions. Then the Gaussian Inverse Wishart-Probability Hypothesis Density (GIW-PHD) filter in the random matrix framework is utilized to track multiple extended objects in the presence of clutter measurements and missed detections. In view of the invariant extension evolution model and neglected actual measurement noise for the original GIW-PHD filter, this paper adopts a dynamic evolution model and a reasonable measurement noise model. A suitable likelihood function is derived and the new recursive expressions are presented along with the necessary assumptions and approximations. Furthermore, a parameter adaptive improved algorithm based on the GIW-PHD filter is presented, that estimates model parameters adaptively. Simulation results show that estimation error of the parameter adaptive improved algorithm is lower than the original GIW-PHD filter, in the case of closely spaced objects, crossing and maneuvering.

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