A Track-to-Track Association Algorithm with Chaotic Neural Network

摘要：

A great deal of attentions is currently focused on multisensordata fusion. A very important aspect of it is track-to-track association and track fusion in distributed nrultisensor-multitarget environments. The approach based on Hopfield neural network has been developed.But the performance of this approach is limited because Hopfield neural network is often trapped in the local minima. This paper try to solve this problem with an approach based on chaotic neural network (CNN).Furthermore,in order to improve the performance of neural network,the association statistic between tracks from different sensors is modified.Computer simulation results indicate that this approach is more efficient than the algorithm based on continuous Hopfield neural network (CHNN).

关键词： chaotic neural network (CNN) track-to-track association multisensor data fusion

作者: He Bao-lin Mao Zheng Liu Yuan-yuan Wu Liang

作者单位: School of Electronic Information and Control Engineering,Beijing University of technology,Beijing,100124

会议类型: 国际会议

会议名称: 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar(第二届亚太合成孔径雷达会议)

会议地点: 西安

会议语种:英文

页码: 788-791

在线出版日期: 2009-10-26（万方平台首次上网日期，不代表论文的发表时间）