Box-CPHD Extended Target Tracking Based on CFSFDP

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    Regarding the problems of excessive divisions and high computational complexity in the current measurement division method of extended target tracking, this study combines the Clustering by Fast Search and Find of Density Peaks (CFSFDP) with the Box-Cardinalized Probability Hypothesis Density (Box-CPHD) filter to propose a Box-CPHD extended target filtering algorithm based on CFSFDP. The algorithm applies CFSFDP to measurement division and it can clearly divide the interval measurement and remove the clutter measurement with the difference in the measurement information density. Then, the Box-CPHD filter is used for prediction update and target state estimation. The simulation experiment shows that in comparison with the classic distance division method, CFSFDP is employed in the measurement preprocessing of the Box-CPHD extended target algorithm. CFSFDP significantly reduces the running time while achieving the same effect, and in the high-clutter environment after clutter removal, the change in clutter only affects the calculation time of distance division but no longer affects the CFSFDP division. The processing of measurement information with CFSFDP can greatly improve the operating efficiency and the real-time performance of the algorithm. After clutter removal, the accuracy of target position estimation is improved to a certain extent.

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  • Received:December 26,2020
  • Revised:January 25,2021
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  • Online: October 08,2021
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