A track-to-track association algorithm with chaotic neural network

A great deal of attentions is currently focused on multisensor data fusion. A very important aspect of it is track-to-track association and track fusion in distributed multisensor-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).

[1]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[2]  Yuyao He,et al.  Chaotic simulated annealing with decaying chaotic noise , 2002, IEEE Trans. Neural Networks.

[3]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[4]  He You,et al.  New track correlation algorithms in a multisensor data fusion system , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Kate Smith-Miles,et al.  A unified framework for chaotic neural-network approaches to combinatorial optimization , 1999, IEEE Trans. Neural Networks.

[6]  Kate Smith-Miles,et al.  Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research , 1999, INFORMS J. Comput..

[7]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[8]  James Llinas,et al.  An introduction to multi-sensor data fusion , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[9]  Shuzhi Sam Ge,et al.  On parameter settings of Hopfield networks applied to traveling salesman problems , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[10]  Y. Bar-Shalom,et al.  A new relaxation algorithm and passive sensor data association , 1992 .

[11]  He You.  Application of Neural Network in Track Correlation of Distributed Multisensor Systems , 2004 .