Multi-sensor multi-target space-time deviation calibration and fusion method

Abstract

The invention relates to a multi-sensor multi-target space-time deviation calibration and fusion method and a computer readable storage medium. The method comprises the steps: calculating dimension expansion state prediction and dimension expansion state prediction covariance of a target i at the k moment and dimension expansion observation prediction of a sensor s on the target i; defining a mapping p = p (m) for indicating the number of a corresponding target; calculating a cross covariance between the dimension expansion state estimation error of the target i and the dimension expansion state estimation error of the target p (m) at the moment k when m is equal to i + 1,..., i + N1 by adopting a recursive processing strategy; calculating a cross covariance between the dimension expansionstate prediction and the dimension expansion observation prediction and an auto-covariance of the dimension expansion observation prediction; and updating the dimension expansion state estimation andthe dimension expansion state estimation covariance of the target i at the moment k. According to the invention, sequential processing strategies are executed among different targets and different sensors at the same moment, so that the precision of multi-sensor space-time deviation estimation is improved while sequential estimation of each target state is realized.

Description

technical field [0001] The invention relates to the technical field of space target tracking, in particular to a multi-sensor multi-target space-time deviation calibration and fusion method and a computer-readable storage medium. Background technique [0002] Currently, in multi-sensor multi-target tracking systems, the collaborative fusion of data collected from multiple sensors improves the overall system performance. The prerequisite for accurate fusion is to estimate and compensate the space-time deviation in the asynchronous multi-sensor system, otherwise, the space-time deviation may lead to the degradation of the tracking performance of the system, and in severe cases, it will generate repeated tracks. [0003] For the problem of spatial deviation estimation and compensation, existing solutions include real-time quality control method, least square method and maximum likelihood registration method, etc. In addition, some scholars have proposed methods based on Kalman...

AI Technical Summary

Problems solved by technology

Existing sequential processing methods only estimate the sensor spatial bias without simultaneously performing bias compensation and fusion

In addition, these methods all assume that there is no time bias in the sensor

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