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Multiple-model Rao-Blackwellized particle probability hypothesis density filter for multitarget tracking

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Abstract

Multitarget tracking (MTT) is a frequent topic in visual surveillance systems. Although the multiple-model probability hypothesis density (MM-PHD) filter plays an important role in the MTT, both computerized intractability and imprecise estimate are still inevitable. To solve the problems, a novel filter is presented in this paper. Different from the previous work, the Rao-Blackwellized particle filtering algorithm is incorporated with the MM-PHD filter to reduce computational load, where the sequence Monte Carlo method is adopted to estimate the nonlinear state of targets, and the linear state is predicted using the Kalman filter with the information embedded in the estimated nonlinear state. With respect to tracking precision, we find that the reweighting scheme can be realized for the numberestimate of both undetected targets and false alarms. The result is useful in balancing the required particle number in order to stabilize target estimates during the surveillance period. The illustrative simulation is finally provided to show the effectiveness of the proposed filter.

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Correspondence to Bo Li.

Additional information

Bo Li received his B.S. degree in Communication and Information Systems from Liaoning University of Technology, China in 2005. He is currently an associate professor in Liaoning University of Technology, China, and is pursuing a Ph.D. at College of Information Science and Technology, Dalian Maritime University, China. His research interests include signal processing, communication systems, state estimates and information fusion.

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Li, B. Multiple-model Rao-Blackwellized particle probability hypothesis density filter for multitarget tracking. Int. J. Control Autom. Syst. 13, 426–433 (2015). https://doi.org/10.1007/s12555-014-0148-7

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  • DOI: https://doi.org/10.1007/s12555-014-0148-7

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