Target grouping method based on fuzzy ART division

Abstract

The invention discloses a target grouping method based on fuzzy ART division and relates to the situation estimation technology field. The method comprises steps that 1, target position measurement data and target identification attribute data are read in; 2, a grouping target number scale is reduced through target identification attribute division to improve grouping efficiency; 3, scale difference is eliminated through division data pre-processing, target space division based on fuzzy ART is further employed to effectively filter noise interference, and grouping accuracy is improved; and 4, a grouping result is outputted. The method is advantaged in that problems of the unknown classification number and noise interference existing in a target grouping method in the prior art are mainly solved, effect, accurate, real-time and dynamic grouping can be realized for multiple formation group targets under the condition of the unknown classification number, and the method can be applied to situation estimation and command control systems.

Description

technical field [0001] The invention belongs to the technical field of situation estimation, and particularly relates to a target grouping method based on fuzzy ART (Adaptive Resonance Theory, adaptive resonance theory), which can be used in situation estimation and command and control systems. Background technique [0002] The situation display is an effective way for the commander to obtain information to control the real-time situation, and it provides the basis and support for formulating plans and decisions. If each target is still regarded as an isolated individual, not only there will be information redundancy, but also the dense target identification will cause the problem of information dazzling, so that the commander cannot quickly and directly understand the situation overview. Therefore, it is necessary to aggregate and classify targets with similar characteristics such as recognition attributes and motion parameters, and divide them into several group targets, c...

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Problems solved by technology

[0004] The fuzzy C-means method and the K-means method need to preset the number of categories, which does not match the situation where the number of categories is usually unknown, and the classification results have a strong dependence on the selection of the initial classification center, resulting in poor stability of the classification results;

[0005] The nearest neighbor method realizes grouping by setting a threshold, which is easy to implement and widely used, but lacks an effective threshold selection method, and it is difficult to effectively deal with grouping problems of different measurement scales or situations;

[0006] The ISODATA method realizes dynamic clustering when the number of clusters is unknown by merging and splitting the clustering results, but it uses the distance between the sample and the cluster center as the basis for clustering, which is suitable for solving the problem of clustering samples in spherical clusters, but for situation estimation There are deficiencies in the common linear formation grouping problem in

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