NDLI: Shape selection partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter for extended target tracking

Content Provider	IET Digital Library
Author	Li, Peng Ge, Hong wei Yang, Jin long Zhang, Huanqing
Abstract	The Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter is a promising approach for tracking an unknown number of extended targets. However, it does not achieve satisfactory performance if targets in different sizes are spatially close and manoeuvring because the partitioning methods are sensitive to manoeuvres. To solve this problem, the authors propose the shape selection partitioning (SSP) measurement partitioning algorithm. The proposed algorithm first calculates potential centres and shapes of targets. It then combines each centre with different shapes to divide measurements into subcells. Accordingly, some candidate partitions can be obtained. Finally, it selects the most likely candidate partition and outputs the corresponding subcells. Simulation results show that the application of SSP to the GIW-PHD filter can achieve better performance when targets are spatially close and manoeuvring, which leads to a lower optimal subpattern assignment distance and a higher accuracy of the sum of weights.
Starting Page	1041
Ending Page	1051
Page Count	11
ISSN	17519675
Volume Number	10
e-ISSN	17519683
Issue Number	Issue 9, Dec (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/10/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2015.0503
Journal	IET Signal Processing
Publisher Date	2016-06-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Extended Target Tracking Filtering Method in Signal Processing Filtering Theory Gaussian Inverse Wishart Probability Hypothesis Density Filter Gaussian Processes GIW-PHD Filter Optimal Subpattern Assignment Distance Probability Shape Selection Partitioning Algorithm Signal Processing Theory SSP Measurement Partitioning Algorithm Statistics Target Tracking
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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