Optimization of data fusion method based on Kalman filter using Genetic Algorithm and Particle Swarm Optimization

During the last decades artificial intelligence has been a common theme for new works. In this paper a new method utilizing artificial intelligence is suggested for data fusion. As a case study purposed method is applied for target tracking. This work is an improved form of a recent work introduced in [1], the coefficients are optimized by Genetic Algorithm and Particle Swarm Optimization as two intelligent methods.The applied intelligent method leads to better performance. The results of two optimization algorithms are compared to each other and the suggested method in [1]. Results show two presented method have less error.

[1]  James S. Meditch,et al.  Stochastic Optimal Linear Estimation and Control , 1969 .

[2]  C. J. Harris,et al.  Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion , 2001 .

[3]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[4]  J. A. Roecker,et al.  Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion , 1988 .

[5]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[10]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[11]  Y. Bar-Shalom,et al.  On optimal track-to-track fusion , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[12]  H. Sorenson,et al.  Stochastic optimal linear estimation and control , 1972, IEEE Transactions on Automatic Control.

[13]  Tian Zhi,et al.  Performance Evaluation of Track Fusion with Information , 2002 .