Multi-object tracking and data interconnection method based on whole neighborhood fuzzy clustering

Abstract

The invention provides a new multi-object tracking and data interconnection method based on whole neighborhood fuzzy clustering. According to the method, measurement distribution in a region is determined, a determination matrix is built in combination with a related wave gate of each object, a statistical distance is built according to a plot-track association rule, a predication position of each object serves as a clustering center, a fuzzy clustering method in fuzzy mathematics is used, probabilities of interconnecting a candidate measurement with different objects in a related wave gate are calculated, and through probability weighted fusion, each object state and a covariance are updated. The method achieves great improvement in the aspect of real-time filter tracking, the tracking precision and the effective tracking rate are close to those in a classical JPDA algorithm, and a new solution is provided for a real-time multi-object tracking problem in clutter environment.

Description

technical field [0001] The invention belongs to sensor information fusion technology, relates to the problem of multi-target tracking and data interconnection in data fusion, and provides a new method for multi-target tracking and data interconnection based on all-neighbor fuzzy clustering. Background technique [0002] The problem of data interconnection in the clutter environment has always been a difficult problem in the field of multi-target tracking. Two types of statistical methods commonly used to solve this problem are: nearest neighbor data interconnection algorithm and all neighbor data interconnection algorithm. These two types of algorithms do not consider the case of measurement indistinguishability, and believe that each measurement in the confirmation area is connected to at most one target or clutter, that is, each measurement has a unique source. The nearest neighbor standard filter (Nearest-neighborstandard filter, NNSF) is a typical algorithm in the neare...

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Problems solved by technology

[0004] In the actual tracking environment, the NNSF algorithm is simple to calculate and can effectively track the target in the sparse clutter environment, but for the multi-target tracking problem in the dense clutter environment, the NNSF algorithm has poor tracking effect

The JPDA algorithm can better solve the problem of measuring multiple targets in the interconnected domain in the clutter environment, but its calculation is more complicated, and with the increase of the number of targets in the observation area or the increase of the number of clutter in the confirmation area, the confirmation The splitting of the matrix will cause a combination explosion phenomenon, so the JPDA algorithm is difficult to implement in engineering

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