Smooth constraint unscented Kalman filtering method and target tracking method

Abstract

The invention provides a smoothing constraint unscented Kalman filtering method. The smoothing constraint unscented Kalman filtering method comprises the following steps: 1, acquiring an original prior probability density function of a target state at a current target observation moment according to unscented transformation; 2, calculating a mean value and a variance of an original prior probability through numerical expectation; 3, introducing noise constraint information, and calculating the center of the approximate feasible region to obtain corrected prior probability density; 4, seeking aGaussian distribution mean value and a variance meeting the constraint condition through posteriori iterative optimization, and generating a new Gaussian sigma point meeting the constraint condition;and 5, carrying out weighted calculation on the Gaussian sigma points to complete a filtering process. The smoothing constraint unscented Kalman filtering method has the advantages in the aspects ofaccuracy and robustness, and meanwhile, the real-time performance of the smoothing constraint unscented Kalman filtering method is superior to that of a particle filtering algorithm. Correspondingly,the invention further provides a target tracking method.

Description

technical field [0001] The present invention relates to the technical field of target tracking, in particular to a smoothing constrained unscented Kalman filtering method and a target tracking method. Background technique [0002] In scientific fields such as navigation and guidance systems, tracking channel state information of rapidly changing wireless channels, and tracking the real-time position of aircraft, it is often necessary to use filtering techniques (such as Kalman filtering, etc.) to achieve real-time tracking of targets. [0003] After a large number of searches, some typical existing technologies are found. For example, the patent application number 201810661518.8 discloses a moving target tracking method based on a switch Kalman filter. The tracking error caused by the mutation of the motion state has a stable tracking effect and good robustness. Another example is the patent application number 201410134331.4 which discloses a target tracking method and an e...

AI Technical Summary

Problems solved by technology

[0004] It can be seen that for target tracking using Kalman filtering, there are still many practical problems that need to be dealt with in practical applications (such as improving the accuracy and robustness of target tracking, etc.) and there are still many specific solutions that have not been proposed.

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