An extended target particle probability hypothesis density filter based on the Star-Convex shape estimation

DOI

10.2991/icmmct-17.2017.233How to use a DOI?

Keywords

Information Fusion; Target Tracking; Track Initiation; Measurement Partition

Abstract

The extended target is characterized by common centroid kinematic state and extended information, extended forms not only can be treated as a state to be estimated separately, including the size, shape and direction information will effectively enhance the performance of filter with proper use. For this reason, a new algorithm of the extended target particle probability hypothesis density filter modeling for Star-Convex is proposed, the algorithm take local clustering trend analysis into account and propose a method of extended target track initiation based on Star-Convex gate, then, according to the different characteristics of measurement sets, we propose an adaptive measurement partition algorithm based on extended information of Star-Convex. Simulation results show that the false initiation and computational cost both reduce significantly. In the intersection or the neighbor target tracking scenario, the proposed partition algorithm can maintain a better performance and improve the stability of the filter.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhuo Cao
AU  - Xinxi Feng
AU  - Lei Pu
PY  - 2017/04
DA  - 2017/04
TI  - An extended target particle probability hypothesis density filter based on the Star-Convex shape estimation
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 1190
EP  - 1196
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.233
DO  - 10.2991/icmmct-17.2017.233
ID  - Cao2017/04
ER  -