Dynamic weighted track fusion algorithm based on track comparability degree

Weighted track fusion algorithm is a common algorithm in distributed system track fusion. How to allot the optimal weighting factors of local tracks is an issue of interest in weighted track fusion algorithm study. A dynamic weighted track fusion algorithm based on track comparability degree is proposed for track fusion in distributed multi-sensor tracking fusion system. The algorithm integrates measurement errors and performance of all sensors. Combining with track statistical distance and Fuzzy Clustering Method, track fuzzy membership comparability degree matrix at every measurement time is established. Real time and dynamic weighting factors of all local tracks in fusion center are allotted correctly, and thus the system can track the target effectively. Performance of fusion track is compared to that of each individual sensor. Simulation results show that fusion track has better tracking accuracy and demonstrate the validity of proposed algorithm.

[1]  Ashraf M. Aziz,et al.  Effects of fuzzy membership function shapes on clustering performance in multisensor-multitarget data fusion systems , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[2]  Yang Zhang,et al.  A Fuzzy Fusion in Multitarget Tracking with Multisensor , 2007, 2007 International Conference on Computational Intelligence and Security (CIS 2007).

[3]  Zhang Nawen.  An Improved Weighted Fusion Algorithm , 2008 .

[4]  Wei Xue,et al.  Adaptive Weighted Fusion Algorithm for Monitoring System of Forest Fire Based on Wireless Sensor Networks , 2010, 2010 Second International Conference on Computer Modeling and Simulation.

[5]  Wang Zhi.  The theory of multi-sensor system data fusion based on linear least square estimation , 2003 .

[6]  Chongquan Zhong,et al.  A Weighted Fusion Algorithm of Multi-sensor Based on Optimized Grouping , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[7]  Ashraf M. Aziz,et al.  Fuzzy track-to-track association and track fusion approach in distributed multisensor-multitarget multiple-attribute environment , 2007, Signal Process..

[8]  Li Hui,et al.  Asynchronous Track-to-Track Fusion Algorithms for Distributed Multi-sensor System , 2006, The Proceedings of the Multiconference on "Computational Engineering in Systems Applications".

[9]  Shuli Sun,et al.  Multi-Sensor Weighted Fusion Suboptimal Filtering for Systems with Multiple Time Delayed Measurements , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[10]  W.D. Blair,et al.  Simulations studies of multisensor track association and fusion methods , 2006, 2006 IEEE Aerospace Conference.

[11]  Gangfeng Yan,et al.  Multi-Sensors Data Tracking Fusion Based on a Neural Network Filter , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[12]  He Liu,et al.  Dynamic System Modeling with Multilayer Recurrent Fuzzy Neural Network , 2007 .

[13]  Li-Wei Fong.  An adaptive filter for multi-sensor track fusion , 2008, 2008 9th International Conference on Signal Processing.