Heterogeneous multi-sensor multi-target tracking method

Abstract

The invention provides a heterogeneous multi-sensor multi-target tracking method, which can rapidly start a target track, reduce calculation amount and improve track tracking accuracy and continuity.The method is realized through the following technical scheme: a track initiation and maintenance module, a track trimming and confirmation module, a hypothesis generation and management module, a track updating and merging module and a track management output module are arranged in a heterogeneous multi-sensor multi-target tracking system, the track initiation and maintenance module roughly classifies measurement data according to attribute characteristics and position grids of the measurement data, and according to the rough classification result, measuring points falling into an existing target track tracking wavegate are sent to the track trimming and confirmation module as candidate point tracks, the tracks confirmed by sequence probability ratio inspection are sent to the hypothesisgeneration and management module for track clustering and hypothesis generation, a target filtering model is determined through the track updating and merging module, the tracks are updated through filtering, and latest covariance information is output through the track management output module.

Description

technical field [0001] The present invention relates to heterogeneous multi-sensor fusion technology in the field of multi-target tracking, such as robots and intelligent instrument systems, information processing, image analysis and understanding, target detection and tracking, automatic target recognition, multi-source image compounding, and heterogeneous multi-sensor multi-target Tracking method. Background technique [0002] With the rapid development of information theory, cybernetics, computer technology, network technology, and sensor technology, the information entering the system has the characteristics of large amount of data, multiple sources, and multiple levels of correlation. Therefore, in the information processing center, the simple accumulation of multi-source observation information will not be allowed. The information processing center should effectively fuse and process multi-source observation information. The outstanding advantages and great success o...

AI Technical Summary

Problems solved by technology

However, in the existing research, the MHT algorithm mostly uses the target position and state for data association to complete target tracking. How to effectively use multiple measurement data, especially signal characteristic data, coordinated by multiple sensors for tracking is the key

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