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

• Main Authors: Xiao Chen, Yaan Li, Yuxing Li, Jing Yu, Xiaohua Li
• Format: Article
• Language: English
• Published: MDPI AG 2016-12-01
• Series: Sensors
• Subjects: probabilistic data association (PDA); joint probabilistic data association (JPDA); interactive multi-model (IMM); combined interactive multiple model probabilistic data association (C-IMM-PDA)
• Online Access: http://www.mdpi.com/1424-8220/16/12/2180

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.