A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment
AbstractThe 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
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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.
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(12):2180.Chicago/Turabian Style
Chen, Xiao; Li, Yaan; Li, Yuxing; Yu, Jing; Li, Xiaohua. 2016. "A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment." Sensors 16, no. 12: 2180.
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