Title: Multiple-model multiple hypothesis probability hypothesis density filter with blind zone

Authors: Tianli Ma; Xinmin Wang; Ting Li

Addresses: Department of Automation, Northwestern Polytechnical University, Chang An-710129, Xi'An, China ' Department of Automation, Northwestern Polytechnical University, Chang An-710129, Xi'An, China ' Department of Automation, Northwestern Polytechnical University, Chang An-710129, Xi'An, China

Abstract: According to the problem of continuous tracking of multiple manoeuvring targets under the blind zone, a multiple-model probability hypothesis density (MM-PHD) filter based on the multiple hypothesis method is proposed. Under the blind zone, target state is estimated using the multiple model method. The hypothesis state is as the update state in the update step. Once the measurement is received, it will be updated using the MM filter. An improved pruning and merging algorithm is proposed to solve the problem of the number of hypothesis Gaussian components increasing continuously. Simulation results show that the novel algorithm is more effective in tracking multiple manoeuvring targets and improves performance of continuous target tracking.

Keywords: blind zone; multiple manoeuvring targets; Gaussian mixture; finite set statistics; intensity function; PHD; multiple model method; multiple hypothesis method; Bayesian theory.

DOI: 10.1504/IJISE.2017.086271

International Journal of Industrial and Systems Engineering, 2017 Vol.27 No.2, pp.180 - 195

Received: 28 Jul 2015
Accepted: 19 Sep 2015
Published online: 04 Sep 2017 *

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