A Doppler Radar Tracking Method for Multiple Maneuvering Targets Based on Gaussian Mixture Probability Hypothesis Density Filtering

Abstract

The method belongs to the field of radar target tracking, in particular to a Doppler radar tracking method for multiple maneuvering targets based on the assumption of Gaussian mixture probability density. In this paper, pseudo-measurement is firstly introduced to replace the target radial velocity measurement obtained by Doppler radar, and then a measurement conversion method based on predicted value information is introduced to deal with position measurement and pseudo-measurement. related. Then, the Gaussian mixture probability hypothesis density filtering method is used, and with the help of the multi-model architecture, the Gaussian components of the surviving, new and derived targets are processed differently according to the correlation between the Gaussian component and the model. For model-independent Gaussian components, namely new and derived Gaussian components, the state is directly estimated; for model-related Gaussian components, that is, surviving Gaussian components, the model probability of each model filter and the model conditional distribution of update components are obtained first. The model is then fused with the conditional distribution of the update components to obtain a state estimate. Among them, when filtering the weight, mean, covariance, etc. of the Gaussian components, sequential filtering is introduced, and the position estimate is obtained first according to the position measurement; then the position estimate is sequentially processed by the pseudo measurement to obtain the final state estimate.

Description

technical field [0001] The method belongs to the field of radar target tracking, and in particular relates to a Doppler radar tracking method for multiple maneuvering targets 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 weighted combination of each measurement that may origin...

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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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