Radar multi-target tracking optimization method based on chaotic neural network

Abstract

The invention discloses a radar multi-target tracking optimization method based on the chaotic neural network. The method is mainly characterized in that state one-step prediction of a tth target at the k time, measurement prediction of the tth target at the k time, the measurement prediction information of a j'th measurement for the tth target at the k time, a one-step prediction error covariance matrix of the tth target at the k time, an information covariance matrix of the tth target at the k time, Kalman gain of the tth target at the k time, an nk*T'-dimension measurement-target association matrix at the k time, an (nk+1)*T'-dimension effective likelihood function matrix of association of nk meausurements and T' targets at the k time, an (nk+1)*T'-dimension normalization matrix of association of the nk meausurements and the T' targets at the k time, an (nk+1)*T'-dimension accurate probability matrix of association of the nk meausurements and the T' targets at the k time, a state equation of the tth target at the k time and an error covariance matrix of the tth target at the k time are sequentially calculated; the t is respectively made to be 1 to T', the error covariance matrix of the T'th targets at the k time is acquired, and real-time tracking for the T'th targets is carried out by a radar according to the error covariance matrix of the T'th targets at the k time.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a radar multi-target tracking optimization method based on a chaotic neural network, which is suitable for real-time tracking of multiple targets by a radar in a clutter environment. Background technique [0002] In recent years, with the complex and changeable application environment, radars are required to have multi-target tracking capabilities and can simultaneously realize multi-target tracking; the basic concept of multi-target tracking was proposed by Wax in an article in the Journal of Applied Physics in 1955. After that, in 1964, Steer published a paper entitled "The Optimal Data Association Problem in Surveillance Theory" on IEEE, which became the forerunner of multi-target tracking, but at that time Kalman filtering was not yet widely used, Steer The track bifurcation method is used to solve the data association problem; in the early 1970s, in the presence of...

AI Technical Summary

Problems solved by technology

The chaotic neural network is used to solve the data association problem in multi-target tracking, which overcomes the shortcomings of using Hopfield network to solve the data association in multi-target tracking, which is easy to fall into local minimum points and slow convergence speed.

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