Target‐tracking algorithm based on improved probabilistic data association

When tracking a single manoeuvring target in clutter environment, when the number of effective measurements within the detection threshold is small, it usually has a greater and more obvious impact on target‐tracking results. If the observation data error is large at this time, the tracking position and speed error will be larger. To solve this problem, a target‐tracking algorithm based on improved probabilistic data association is proposed in this paper. By dynamically adjusting the detection threshold, the effective quantity within the detection threshold of each frame is basically stable. Simulation results show that the improved algorithm is more accurate in location and speed than the traditional probabilistic data association method and Kalman filter, and the availability and effectiveness of the algorithm are verified. By dynamically adjusting the detection threshold, the effective quantity within the detection threshold of each frame is basically stable. Simulation results show that the improved algorithm is more accurate in location and speed than the traditional probabilistic data association method, and the availability and effectiveness of the algorithm are verified.