Detection information filtering method for radar target tracking

Abstract

The invention discloses a detection information filtering method for radar target tracking. The detection information filtering method comprises steps as follows: S1, when radar tracks a target, to-be-filtered detection information and corresponding SNR (signal-to-noise) information are acquired from target detection information returned by detection each time; S2, the acquired to-be-filtered detection information is subjected to Kalman filtering, and when the designated amount of detection information is acquired, the step S3 is executed; S3, the designated amount of continuous detection information containing detection information at the current moment is acquired each time, part of data values are processed according to the data value change trend, and final detection information whichis acquired according to the detection information after integrated data processing and the corresponding SNR information is subjected to Kalman filtering until filtering of all detection formation iscompleted. The method has the advantages that the implementation is simple, influence caused by filtering signal fluctuation during Kalman filtering can be reduced, the filtering effect is good, theradar detection precision is high, and the like.

Description

technical field [0001] The invention relates to the technical field of radar signal processing, in particular to a detection information filtering method for radar target tracking. Background technique [0002] Target tracking is an important part of radar signal processing, which is to establish and maintain a tracking track for the target to be tracked according to the detection information provided by the radar front end. In the process of target tracking, due to the existence of noise and other interference sources in the actual environment, the detection information provided by the radar front end cannot truly represent the actual movement information of the target, and sometimes even has a huge deviation, so the detection information should be filtered at this time. The artificial filtering algorithm is used to minimize the influence of noise on the detection value, so that the detection value is as close as possible to the real motion information of the target after f...

AI Technical Summary

Problems solved by technology

The recursive process of Kalman filtering is as follows figure 1 As shown, in the filtering calculation circuit, the innovation process α(n) is directly equal to the radar observation value z(n) minus the filtering algorithm prediction value, that is, the calculation of the innovation process α(n) depends on the observation signal z( n), and in practical applications, due to the existence of noise and the influence of other disturbances, the observation value z(n) returned by the radar will fluctuate each time, especially when the target is far away from the radar, the observation value z(n) The fluctuation will be more severe. For example, the farther the distance, the lower the signal-to-noise ratio, which will seriously affect the accuracy of radar detection. When the fluctuation of the observed value z(n) exceeds the tolerance range of the Kalman filter, the It will cause the filtering results to diverge, so that the filtering will lose its effect, and then affect the entire target tracking system

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