Multi-target Classification Tracking Algorithm Based on Adaptive Observation Recognition
In realistic multi-target tracking tasks,the performance of the probability hypothesis density filter is obviously disturbed by clutter,which greatly restricts the accuracy and efficiency of target tracking.To overcome this problem,an adaptive observation recognition-based multi-target classification tracking algorithm is proposed within the framework of the probability hypothesis density filter.Firstly,according to the importance of each object,the adaptive observation recognition strategy uses adaptive threshold to identify the observations from the existing object,the newly emerged object and the clutter from each time step observation.Then,the target classification update strategy uses different target observation classification to update the prediction strength of existing targets and the prior strength of new targets,which reduces the interference of different types of target observation mixed on the posterior intensity accuracy of targets.The effectiveness and robustness of the proposed algorithm are verified by the simulation target scenario.
adaptive thresholdobservation and recognitiontarget classification trackingstate estimationiteration efficiency
万方数据
维普
