Adaptive distributed track data fusion method based on covariance indicator function

Abstract

The invention discloses an adaptive distributed track data fusion method based on a covariance indicator function. The method comprises the following steps: 1) receiving and storing track data, and carrying out pretreatment on the received track data; 2) carrying out space-time registration on the multi-source track data; 3) associating the track data obtained by different sensors and determining correspondence relation between observation data and moving objects; and 4) carrying out fusion on the multi-source track data based on adaptive selection of fusion index, and determining motion state of each moving object. The method can selectively eliminate data having filtering divergence or overlarge system deviation and the like, which hinders improvement of precision of the fusion results, in the fusion process, and keeps the results having good consistency and carries out fusion.

Description

technical field [0001] The invention belongs to the field of track data processing, and in particular relates to an adaptive distributed fusion technology of multi-source track data. Background technique [0002] In order to improve the tracking or detection accuracy of moving targets and increase the measurement of some parameters of the measured targets, it has become an inevitable trend to use sensor networks to simultaneously monitor moving targets. Differences between local estimates can be large due to the effect of systematic bias. In an ideal situation, the system bias can be estimated and corrected; however, in the actual complex scene, due to the unknown serious bias, there are multiple hidden time-varying parameters, which makes the correction of the bias very difficult. If not done properly, the inconsistency and contradictory information generated by sensor bias may prevent multi-sensor fusion from achieving the expected results, and information synthesis degra...

AI Technical Summary

Problems solved by technology

Differences between local estimates can be large due to systematic bias

In an ideal situation, the system bias can be estimated and corrected; however, in the actual complex scene, due to the unknown serious bias, there are multiple hidden time-varying parameters, which makes the correction of the bias very difficult

If not done properly, the inconsistency and contradictory information generated by sensor bias may prevent multi-sensor fusion from achieving the desired effect, and information synthesis will reduce the performance of state estimation.

[0003] The core of traditional

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