NDLI: Novel N-scan GM-PHD-based approach for multi-target tracking

Content Provider	IET Digital Library
Author	Dehkordi, Mahdi Yazdian Azimifar, Zohreh
Abstract	The GM-PHD-based filter has been proposed as an alternative of the PHD filter to estimate the first-order moment of the multi-target posterior density. The GM-PHD filter utilises a weighted summation of Gaussian components to estimate the target states. This filter and its recent variants perform state extraction of the targets based on the target weights. However, due to different uncertainties such as noisy observation, miss-detection, clutter or occlusion, the weight of a target is decreased and the estimation of the target is lost in some steps. In this study, the authors develop a simple and effective N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.
Starting Page	493
Ending Page	503
Page Count	11
ISSN	17519675
Volume Number	10
e-ISSN	17519683
Issue Number	Issue 5, Jul (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/10/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0480
Journal	IET Signal Processing
Publisher Date	2016-07-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Filtering Method in Signal Processing Filtering Theory First-order Moment Estimation Gaussian Component Propagation Gaussian Component Weighted Summation Gaussian Processes GM-PHD Filter GM-PHD-based Method Mixture Model Multitarget Posterior Density Multitarget Tracking N-scan Gaussian Mixture Probability Hypothesis Density Based Approach N-scan State Extraction Algorithm Signal Processing Theory State Estimation Statistics Target State Estimation Target State Extraction Target Tracking Target Weight Reduction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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