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Sensors 2016, 16(5), 653;

Cubature Information SMC-PHD for Multi-Target Tracking

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
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
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)
Full-Text   |   PDF [544 KB, uploaded 9 May 2016]   |  


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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