Rao-Blackwellized Free Clustering Particle PHD Filter For Multi-Target Tracking

DOI

10.2991/nceece-15.2016.293How to use a DOI?

Keywords

Rao-Blackwellized Particle Filter; Free Clustering; Particle PHD Filter; Kalman Filter; Linear Component; Non Linear Component; Multi-Target Tracking

Abstract

This paper presents a novel approach to improve the accuracy of auxiliary particle probability hypothesis density filter without the need of clustering step. In most cases, the states of target can be seen as combinations of linear components and non linear ones. The new approach separates the two different components and then adopts auxiliary particle filter (APF) and Kalman filer to estimate the non linear parts and linear parts. After the update step it extracts the multi-target states via the predict particles and their updated weights which correspond to each measurement. The simulation results illustrate the effectiveness of the presented approach.

Copyright

© 2016, 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  - Ping YE
AU  - Hui Chen
PY  - 2015/12
DA  - 2015/12
TI  - Rao-Blackwellized Free Clustering Particle PHD Filter For Multi-Target Tracking
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 1622
EP  - 1629
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.293
DO  - 10.2991/nceece-15.2016.293
ID  - YE2015/12
ER  -