Multi-target tracking method

Abstract

The invention relates to a multi-target tracking method in the technical field of information processing, comprising the following steps of: 1, obtaining the original state value of a track of each tracked target and the number of the tracked targets; 2, initializing the track state value by utilizing a sliding window method; 3, obtaining the synthesizing measurement and the synthesizing covariance of each track; 4, updating the T-frame track state value of each track in a sliding window by utilizing Kalman smoothing; 5. judging whether the Kalman smoothing is converged or not, if not, returning to step 3 until the Kalman smoothing is converged, and otherwise, returning to step 2, starting to process new data and outputting the target state value of first S moments in the sliding window to draw out the motion track of each target; and 6, continuously repeating the process of step 2 to step 5 so that the motion track of each target in all moments is obtained. The multi-target tracking method has the advantages of accurate tracking and high tracking speed and has wide application prospects in the fields of information fusion, radar data processing and multi-target tracking.

Description

technical field [0001] The invention relates to a method in the technical field of information processing, in particular to a multi-target tracking method. Background technique [0002] In the field of modern multi-target tracking, how to associate data between targets and measurements is the core issue. Probabilistic Multi-Hypothesis Tracker (PMHT) considers that the association between targets and measurements is an independent process, that is, the association of a target with a certain measurement does not affect the association of other targets with the measurement. Under such an assumption of the correlation between the target and the measurement, a measurement may be associated with multiple targets, which is inconsistent with reality. However, the simulation results of the probabilistic multiple hypothesis tracking method show that the probability of this situation is very low, so it can be used to solve the problem of data association. At the same time, the probab...

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Problems solved by technology

[0003] However, in the original probabilistic multi-hypothesis tracking method, because the parameter model it adopts believes that all measurement values ​​are generated by a certain target, the calculated posterior correlation probability between the target and the measurement is not consistent with the real The situation is consistent, especially in the scene of tracking a single target, the weight of all current measurement values ​​on the target state value is the same, so in the environment with high clutter density or directional interference, it is easy to lose the weight of the target state value. target tracking

[0004] After searching the existing literature, it was found that Peter Willet, Yanhua Ruan, Roy Streit and others published in "IEEE Transactions on Aerospace a

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