关键词: 自组织竞争, 神经元优化, 阈值, 坏死神经元, 聚类, 航迹关联

Abstract: There are several difficulties must be overcome to develop a track-to-track association algorithm in a distributed multi-sensor situation. For example, it might get errors and omissions related track when there are cross or bifurcation in the target moving patch. Besides, the computation might grow rapidly with the increasing of the number of sensors and target. In order to resolve these problems, a modified Kohonen neural network based correlation algorithm was presented. The algorithm was made up of four main modules, including clustering association, target state estimation, neurons optimization, and state fusion estimation. The suitable thresholds for each layer of the competition element in the neural network were set to avoid the phenomenon of necrotic neurons occurred in the conventional method due to inappropriate choice of initial weights. The learning rules were also designed for the clustering of multi-sensor measurement data in a self-organizing manner. Besides, data clustering association on continuous-time data were used to achieve moving target track correlation. The simulation results demonstrate the efficiency and effectiveness of the proposed algorithm.

Key words: self-organizing competition, neuron optimization, threshold, necrotic neuron, clustering, track correlation