Centralized multi-sensor column target particle filtering algorithm based on shape and direction descriptors

Abstract

In order to meet the engineering demand that targets in a column are accurately tracked through multiple sensors under the complicated background of cloud and rain clutter, banding interference and the like and overcome the defect that a traditional multi-sensor multi-target tracking algorithm and an existing column target tracking algorithm both have difficulty in achieving an ideal tracking effect, the invention provides a centralized multi-sensor column target particle filtering algorithm based on shape and direction descriptors according to the characteristic that true echo space structures of the targets in the same non-motorized column at adjacent moments are fixed relatively. According to the centralized multi-sensor column target particle filtering algorithm based on the shape and direction descriptors, state update of multi-dimensional trace points of the targets in the column is achieved based on particle filtering according to redundancy trace points in graph similarity removal state prediction, and the targets in the centralized multi-sensor column are tracked accurately.

Description

1. Technical field [0001] The invention belongs to the technical field of multi-sensor multi-target information fusion and provides a centralized multi-sensor formation target tracking algorithm under complex background. 2. Background technology [0002] The traditional multi-sensor multi-target tracking algorithm has very limited tracking effect on formation targets. This type of algorithm is usually based on the measurement and directly builds the navigation of the targets in the formation. However, due to the small distance between the targets in the formation, the tracking gates of each target will overlap seriously, and the difficulty of data interconnection is greatly increased; and because the behavior patterns of the targets in the formation are similar, False track initiation and maintenance can accumulate over time and cause serious confusion in the overall situation. [0003] In recent years, scholars at home and abroad have proposed a series of formation target ...

AI Technical Summary

Problems solved by technology

This type of algorithm reduces the probability of tracking confusion and calculation explosion to a certain extent, improves the stability of the entire tracking system, and saves a lot of radar resources. However, with the improvement of sensor resolution, it gradually shows the following shortcomings: First, the derivation environment of the algorithm is mostly relatively simple, and it is usually assumed that the individual targets in the formation are completely identifiable. However, in reality, due to factors such as mutual occlusion of targets and environmental interference, the formation targets are usually partially identifiable; second, in In some practical engineering applications, while tracking the entire formation, individual targets in the formation need to be tracked individually; third, in order to effectively improve the precise tracking effect of targets in the formation, it is necessary to use multiple sensors in engineering to observe from different directions Formation targets, however, the existing algorithms only consider the single-sensor situation, and there is no research on the complex multi-sensor situation

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