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

A multi-target tracking and neural network technology, applied in instruments, data processing applications, radio wave measurement systems, etc., can solve the problems of local minimum point convergence speed and other problems, and achieve the effect of high convergence speed and high optimization rate

Active Publication Date: 2017-07-21
XIDIAN UNIV
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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.

Method used

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

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Embodiment Construction

[0028] refer to figure 1 , is a flow chart of a radar multi-target tracking optimization method based on a chaotic neural network of the present invention; wherein the chaotic neural network-based radar multi-target tracking optimization method comprises the following steps:

[0029] Step 1. Determine the total number of targets tracked by the radar as T′, and determine the total number of measurements corresponding to time k as n k , and respectively record the state estimation of the t-th target at time k-1 as Denote the state error covariance matrix of the t-th target at time k-1 as P t (k-1|k-1), the state transition matrix of the t-th target at time k-1 is recorded as F t (k|k-1), the measurement matrix of the t-th target at time k is recorded as H t (k), the process noise covariance matrix of the t-th target at time k-1 is recorded as Q t(k-1), the measurement noise covariance matrix of the t-th target at time k is denoted as R t (k), and then sequentially calculat...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41G06Q10/04
Inventor 王彤李杰
Owner XIDIAN UNIV
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