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Open AccessArticle

Cubature Information SMC-PHD for Multi-Target Tracking

by Zhe Liu 1,2, Zulin Wang 1,3 and Mai Xu 1,*
1
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
3
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Sensors 2016, 16(5), 653; https://doi.org/10.3390/s16050653
Received: 6 March 2016 / Revised: 27 April 2016 / Accepted: 1 May 2016 / Published: 9 May 2016
(This article belongs to the Special Issue UAV-Based Remote Sensing)
In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario. To enhance the performance of the conventional SMC-PHD filter, we propose in this paper two approaches using the cubature information filter (CIF) for multi-target tracking. More specifically, we first apply the posterior intensity as the IS function. Then, we propose to utilize the CIF algorithm with a gating method to calculate the IS function, namely CISMC-PHD approach. Meanwhile, a fast implementation of the CISMC-PHD approach is proposed, which clusters the particles into several groups according to the Gaussian mixture components. With the constructed components, the IS function is approximated instead of particles. As a result, the computational complexity of the CISMC-PHD approach can be significantly reduced. The simulation results demonstrate the effectiveness of our approaches. View Full-Text
Keywords: Sequential monte carlo; probability hypothesis density; importance sampling; cubature information filter; Gaussian mixture; J0101 Sequential monte carlo; probability hypothesis density; importance sampling; cubature information filter; Gaussian mixture; J0101
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Liu, Z.; Wang, Z.; Xu, M. Cubature Information SMC-PHD for Multi-Target Tracking. Sensors 2016, 16, 653.

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