Kalman-gain aided particle PHD filter for multi-target tracking

We propose an efficient SMC-PHD filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error (MSE) between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurat...

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Bibliographic Details

Main Authors:	Abdullahi Daniyan, Yu Gong, Sangarapillai Lambotharan, Pengming Feng, Jonathon Chambers
Format:	Default Article
Published:	2017
Subjects:	Aerospace engineering not elsewhere classified Mechanical engineering not elsewhere classified Kalman-gain Particle filter Sequential Monte Carlo (SMC) Probability hypothesis density (PHD) filter Multitarget tracking (MTT) Bayesian tracking Aerospace Engineering Mechanical Engineering not elsewhere classified
Online Access:	https://hdl.handle.net/2134/24704
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