Track identifying method of probability hypothesis density filter and track identifying system

Abstract

The invention provides a track identifying method of a probability hypothesis density filter suitable for the technical field of multi-sensors. The track identifying method includes the first step of determining a predicted gauss item and an identity identification of the gauss item according to a gauss item and an identity identification of the gauss item at the previous moment and adding a non-exclusive identity identification to each newly produced gauss item, the second step of determining updated gauss items and identity identifications of the updated gauss items according to the predicted gauss item, the newly produced gauss items and the corresponding identity identifications, the third step of cutting and combining the updated gauss items and the identity identifications, the fourth step of regulating the identity identifications whose weight is larger than a preset weight threshold value according to the cut and combined gauss items and the corresponding identity identifications, and the fifth step of extracting the gauss items whose weight is larger than the preset weight threshold value to serve as output of the filter and outputting the corresponding identity identifications. According to the method, the identity identifications are added to the gauss items, target states of different moments are correlated, and accordingly motion trails of targets are obtained.

Description

technical field [0001] The invention relates to a multi-sensor information fusion method, in particular to a trajectory identification method and a trajectory identification system of a probability hypothesis density filter. Background technique [0002] In the presence of false alarms, missed detections and unknown number of targets, the probability hypothesis density filter proposed by Mahler is a new method to solve target detection and tracking. The probability hypothesis density filter avoids the direct correlation between the observed value and the state value, and its biggest advantage is that the target number can be estimated from the posterior moment. In order to solve the problem that the integral operation in the prediction and update equation of the probability hypothesis density filter is difficult to handle, Vo et al. proposed the particle probability hypothesis density filter and the Gaussian mixture probability hypothesis density filter. At present, the pro...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a trajectory identification method and trajectory identification system of a probability hypothesis density filter, aiming at solving the problem that the target trajectory is difficult to determine when the probability hypothesis density filter is used to track multiple targets

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