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Sensors 2016, 16(12), 2180; doi:10.3390/s16122180

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
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Author to whom correspondence should be addressed.
Academic Editor: Joonki Paik
Received: 20 October 2016 / Revised: 10 December 2016 / Accepted: 14 December 2016 / Published: 18 December 2016
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Abstract

The problem of data association for target tracking in a cluttered environment is discussed. In order to improve the real-time processing and accuracy of target tracking, based on a probabilistic data association algorithm, a novel data association algorithm using distance weighting was proposed, which can enhance the association probability of measurement originated from target, and then using a Kalman filter to estimate the target state more accurately. Thus, the tracking performance of the proposed algorithm when tracking non-maneuvering targets in a densely cluttered environment has improved, and also does better when two targets are parallel to each other, or at a small-angle crossing in a densely cluttered environment. As for maneuvering target issues, usually with an interactive multi-model framework, combined with the improved probabilistic data association method, we propose an improved algorithm using a combined interactive multiple model probabilistic data association algorithm to track a maneuvering target in a densely cluttered environment. Through Monte Carlo simulation, the results show that the proposed algorithm can be more effective and reliable for different scenarios of target tracking in a densely cluttered environment. View Full-Text
Keywords: probabilistic data association (PDA); joint probabilistic data association (JPDA); interactive multi-model (IMM); combined interactive multiple model probabilistic data association (C-IMM-PDA) probabilistic data association (PDA); joint probabilistic data association (JPDA); interactive multi-model (IMM); combined interactive multiple model probabilistic data association (C-IMM-PDA)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, X.; Li, Y.; Li, Y.; Yu, J.; Li, X. A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment. Sensors 2016, 16, 2180.

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