Maneuvering target tracking loss often occurs with the traditional association gate design method, and the probabilistic data association algorithm is not fit for multi-cross target tracking. To overcome these problems, a cognitive radar data association algorithm is proposed based on the human visual attention model and the perceptual object "feature integration" theory. The algorithm is based on the comprehensive interacting multiple model probabilistic data association algorithm, and uses the "current" statistical model, in which the maximum maneuvering level is known, and the uniform motion model as the model set. The association gate can be adjusted via target maneuvering, balancing the radar computing time and tracking success rate. Based on the features of target location, the motion features of the target are extracted and integrated to classify the public measure in the cross area of the association gate. The multi-cross target tracking problem is then transferred into the problem of tracking of multiple single targets, so the traditional probabilistic data association algorithm is optimized. Simulation results show that the proposed algorithm can significantly reduce the tracking failure rate and computing time, and the optimized data association algorithm with slightly more time consumption is more environment adaptable than the traditional algorithm.
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