|An Improved ET-GM-PHD Filter for Multiple Closely-Spaced Extended Target Tracking
Jinlong Yang*, Peng Li, Le Yang, and Hongwei Ge
International Journal of Control, Automation, and Systems, vol. 15, no. 1, pp.468-472, 2017
Abstract : "This paper presents an enhanced version of the ET-GM-PHD algorithm, a recently developed multiple
extended target tracking (METT) technique. The original ET-GM-PHD filter tends to underestimate the target
number, because the likelihood estimate in the state update process may poorly approximate the real one when
targets are close to each other. The proposed algorithm addresses this drawback via introducing a new penalty
strategy in estimating the measurement likelihood. Besides, Gaussian component labeling technique is adopted to
obtain individual target tracks. Simulations show that for closely-spaced extended target tracking, the improved
method achieves track continuity and exhibits better estimation accuracy over the original ET-GM-PHD filter."
"Closely-spaced targets, multiple extended target tracking (METT), probability hypothesis density (PHD), underestimation problem."
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