Multi-maneuvering-target Doppler radar tracking method based on Gaussian mixture probability hypothesis density filtering

Abstract

The invention belongs to the field of radar target tracking, and particularly relates to a multi-maneuvering-target Doppler radar tracking method based on Gaussian mixture probability density hypothesis. The method comprises the steps: firstly, introducing pseudo measurement to replace target radial velocity measurement obtained by a Doppler radar, then, introducing a measurement conversion methodbased on predicted value information to process position measurement and pseudo measurement, and meanwhile, carrying out decorrelation on the pseudo measurement and the position measurement; by adopting a Gaussian mixture probability hypothesis density filtering method and by means of a multi-model framework, aiming at the correlation between Gaussian components and models, carrying out differentprocessing on the Gaussian components of surviving, newly-born and derivative targets; for Gaussian components irrelevant to the models, namely, the newly-born and derivative Gaussian components, directly estimating the states thereof; for the Gaussian components related to the models, that is, the surviving Gaussian components, obtaining the model probability of each model filter and the model condition distribution of the updated components, and then, fusing the models and the condition distribution of the updated components to obtain state estimation, wherein introducing sequential filtering during filtering of the weight, the mean value, the covariance and the like of the Gaussian components, and obtaining position estimation according to position measurement; and performing sequential processing on the position estimation by using pseudo measurement to obtain final state estimation.

Description

technical field [0001] This method belongs to the field of radar target tracking, especially the multi-maneuvering target Doppler radar tracking method based on Gaussian mixture probability density assumption. Background technique [0002] In the radar target tracking scenario, due to the existence of clutter false alarms, the measurement set does not only contain the echoes of the target. In order to determine the measurement point track for target state update, the nearest neighbor (Nearest neighbor, NN) method selects the point with the closest statistical distance to the predicted position of the target (Arya S. Nearest Neighbor Searching and Applications. Ph.D. thesis, University of Maryland , College Park, MD, 1995.). This method is prone to association errors when the clutter density is large. The probability data association (Probability Digital Association, PDA) technology combines the measurement weights of each possible origin of the target to update the state o...

AI Technical Summary

Problems solved by technology

[0006] Second, the problem of target mobility

When the radar target is being tracked, the target motion mode is unknown, and the target may not only continue to move in a certain mode, and there is a possibility of maneuvering. At this time, the actual motion of the target will not match the motion modeling, and even eventually lead to the tracking process. lost target

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