Target tracking method and tracking system for nonlinear Gaussian system

Abstract

The invention is applicable to the field of multi-sensor information fusion, and provides a target tracking method and a tracking system for nonlinear Gaussian systems. The steps of the method are as follows: first, according to the marginal distribution and the existence probability of each target at the previous moment, predict the marginal distribution and the existence probability of each target at the current moment, and specify the corresponding marginal distribution and the existence probability for the new target at the current moment . Combined with the position measurement at the current moment, Bayesian rule is used to obtain the updated marginal distribution and existence probability of each target at the current moment, and then the updated marginal distribution related to each target is cut and merged to obtain the marginal distribution of each target at the current moment and its existence probability, and finally cut out the edge distributions whose existence probability is less than the first threshold, and use the remaining edge distributions and their existence probabilities as the recursive input at the next moment, and at the same time, extract the edges whose existence probability is greater than the second threshold distribution as the output at the current moment.

Description

technical field [0001] The invention belongs to the technical field of multi-sensor information fusion, and in particular relates to a target tracking method and a tracking system for a nonlinear Gaussian system. Background technique [0002] Multi-target Bayesian filter and probabilistic hypothesis filter are effective methods for target detection and tracking. These two types of filters pass the joint distribution of multiple target states or the first moments of the joint distribution, respectively, during the recursive process. In actual use, we found that these two filters have the following two difficult problems: First, when the target distance is very close, the output of these two filters is the mean value of the multi-target state, so that the two filters It is difficult to distinguish the targets that are very close; the second is that the recursion of the filter involves an integral operation problem that is difficult to handle, and in a nonlinear system, there ...

AI Technical Summary

Problems solved by technology

In actual use, we found that these two filters have the following two difficult problems: First, when the target distance is very close, the output of these two filters is the mean value of the multi-target state, so that the two filters It is difficult to distinguish the targets that are very close; the second is that the recursion of the filter involves an intractable integral operation problem, and in a nonlinear system, there is no closed-form expression for the integral operation

Images