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

作者：Chen, Xiao; Li, Yaan^*; Li, Yuxing; Yu, Jing; Li, Xiaohua

来源：Sensors (Switzerland), 2016, 16(12): 2180.

DOI：10.3390/s16122180

• probabilistic data association (PDA)
• joint probabilistic data association (JPDA)
• interactive multi-model (IMM)
• combined interactive multiple model probabilistic data association (C-IMM-PDA)

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.

• 出版日期2016-12
• 单位西北工业大学