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PHD for Composite Tracking Algorithm Based on Asynchronous Multi-sensor Multi-target Measurements
Received date: 2012-12-25
Revised date: 2013-07-11
Online published: 2013-09-05
The current composite tracking algorithms are computationally intractable and may lose target tracks in the undetected region. In order to solve these problems, a new composite tracking algorithm based on the probability hypothesis density (PHD) algorithm is proposed. The detection region is divided into a one-sensor region, a multiple-sensor region and an undetected region. Multi-sensor PHD filters for the regions are constructed using finite sets statistics theory (FISST). Tracking initiation and tracking maintenance methods for different regions are presented. Finally, the closed-form solutions to the PHD composite tracking algorithm are derived under the linear-Gaussian conditions. Compared with the product multi-sensor PHD, simulation results show that the proposed algorithm has lower computational complexity and better estimation of target states, which indicates its good prospect for application in engineering fields.
WU Xinhui , HUANG Gaoming , GAO Jun . PHD for Composite Tracking Algorithm Based on Asynchronous Multi-sensor Multi-target Measurements[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(12) : 2785 -2793 . DOI: 10.7527/S1000-6893.2013.0338
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