Improved Kalman filtering method used for target tracking

Abstract

The invention relates to an improved Kalman filtering method used for target tracking. The method comprises the following steps that S1, the motion equation of the observation target is established, the motion equation includes the dynamic equation and the observation equation, and the observation value of the target is obtained according to the observation equation; S2, the filtering value of thetarget position information after Kalman filtering is obtained; S3, the threshold is set and the residual error of the filtering value and the observation value is calculated, and the step S4a or S4bis performed according to the relationship between the threshold and the residual error; S4a, if the residual error is not greater than the threshold, the filtering value is directly used as the prediction value and the prediction position of the target at the next moment is obtained; S4b, if the residual error is greater than the threshold, weighted summation is performed on the filtering valueand the observation so as to obtain the updated prediction, and the prediction position of the target at the next moment is obtained according to the updated prediction value; and S5, the steps are repeated so as to obtain the prediction motion tracks of the target. The accuracy of target tracking can be enhanced and the tracking error of maneuvering of the target can be reduced.

Description

technical field [0001] The invention relates to the field of target tracking, in particular to an improved Kalman filter method for target tracking. Background technique [0002] Target tracking has a wide range of applications in both military and civilian fields, such as air surveillance, satellite and spacecraft tracking, and intelligent video surveillance. Accurate tracking of targets is conducive to accurately understanding the position of the other party's targets, and it is a vital core technology for high-tech weapon systems and GPS systems. Accurate positioning and tracking of targets are urgent problems to be solved now. The target tracking problem is actually the tracking and filtering problem of the target state, that is, to accurately estimate the target state according to the target measurement data obtained by the sensor. During the movement, the target changes continuously with parameters such as speed, angle, acceleration, etc., so that the position of the...

AI Technical Summary

Problems solved by technology

Among them, the KF method is simple to calculate, but the accuracy is poor; the EKF method is suitable for weak nonlinear systems, and the process is simple, but there are many shortcomings: for example, when the difference between the posterior mean and the true value is large, it is easy to cause filtering divergence; In most cases, the Jacobian matrix is ​​difficult to calculate, which leads to difficulties in practical applications

The PF method has high precision, but when the number of particles is large, the calculation complexity is too high, which makes it unable to meet the real-time requirements

At the same time, the above tracking methods cannot respond quickly when the target maneuvers, resulting in large tracking errors

[0004] It can be seen that the above-mentioned tracking method has the following defects: poor calculation accuracy or complex calculation, which cannot meet the requirements of precise strike and tracking of the target, and cannot respond quickly to the target's maneuvering

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