Gaussian particle flow implementation of PHD filter

Lingling Zhao; Junjie Wang; Yunpeng Li; Mark J. Coates

doi:10.1117/12.2228326

17 May 2016 Gaussian particle flow implementation of PHD filter

Lingling Zhao, Junjie Wang, Yunpeng Li, Mark J. Coates

Proceedings Volume 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV; 98420D (2016) https://doi.org/10.1117/12.2228326
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Particle ﬁlter and Gaussian mixture implementations of random ﬁnite set ﬁlters have been proposed to tackle the issue of jointly estimating the number of targets and their states. The Gaussian mixture PHD (GM-PHD) ﬁlter has a closed-form expression for the PHD for linear and Gaussian target models, and extensions using the extended Kalman ﬁlter or unscented Kalman Filter have been developed to allow the GM-PHD ﬁlter to accommodate mildly nonlinear dynamics. Errors resulting from linearization or model mismatch are unavoidable. A particle ﬁlter implementation of the PHD ﬁlter (PF-PHD) is more suitable for nonlinear and non-Gaussian target models. The particle ﬁlter implementations are much more computationally expensive and performance can suﬀer when the proposal distribution is not a good match to the posterior. In this paper, we propose a novel implementation of the PHD ﬁlter named the Gaussian particle ﬂow PHD ﬁlter (GPF-PHD). It employs a bank of particle ﬂow ﬁlters to approximate the PHD; these play the same role as the Gaussian components in the GM-PHD ﬁlter but are better suited to non-linear dynamics and measurement equations. Using the particle ﬂow ﬁlter allows the GPF-PHD ﬁlter to migrate particles to the dense regions of the posterior, which leads to higher eﬃciency than the PF-PHD. We explore the performance of the new algorithm through numerical simulations.

Citation Download Citation

Lingling Zhao, Junjie Wang, Yunpeng Li, and Mark J. Coates "Gaussian particle flow implementation of PHD filter", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98420D (17 May 2016); https://doi.org/10.1117/12.2228326

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