A Target Tracking Method Based on Data Fusion of Multiple Kalman Filters

Abstract

The invention discloses a multi-Kalman filter data fusion target tracking method, comprising the following steps: step 1, calculating the uncertain information of each Kalman filter; step 2, determining the uncertainty information of each Kalman filter according to the uncertain information weight; step 3, fusing the target state values ​​of each Kalman filter to obtain the estimated value of the target state. The present invention is simple in structure and reasonable in design, uses uncertain information to generate the weights of each Kalman filter, uses the least squares method to fuse the target state values ​​predicted by multiple Kalman filters at the current moment with the generated weight information, and the final result of the fusion is The estimation value of the target state at the current k time for the target tracking model improves the anti-interference performance of target tracking and makes it have better prediction performance in uncertain environments.

Description

technical field [0001] The invention belongs to the technical field of target tracking, and in particular relates to a target tracking method for multi-Kalman filter data fusion. Background technique [0002] With the advent of the Internet of Everything era, all kinds of electronic devices and sensors have spread in every corner of life, work and work. Combining technologies such as communication and artificial intelligence, people's lives have been greatly improved. At the same time, the upgrading of manufacturing industry and intelligent manufacturing have brought about the update and iteration of industrial production equipment. Automatically produced equipment often needs to have the ability to identify and track items; Applications such as video surveillance and face recognition have become more intelligent, and these applications have also made target recognition and tracking more popular. Therefore, the development of technology has greatly enriched the application ...

AI Technical Summary

Problems solved by technology

However, in the process of target tracking, the sensor is affected by a series of environmental uncertainties such as noise interference in a complex environment, and there may be inconsistencies and conflicts between multi-source data, which will affect the performance of the sensor and cause the observed data to have inconsistencies. However, it is difficult to directly integrate multi-source data. These uncertainties will bring troubles to target recognition and tracking under multi-source data. Therefore, when tracking targets, it is necessary to combine uncertainty theory to deal with uncertain information.

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