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 app

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