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Comparison of Genetic Algorithm and Particle Swarm Optimization for Data Fusion Method Based on Kalman Filter

M. A. Badamchizadeh, A. Nikdel, M. Kouzehgar


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 proposed method is applied on 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. In addition to the weight coefficients introduced in [1], we have applied optimization on sensor gains as well.
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 methods have less error.


Data Fusion, Target tracking, Kalman filter, Genetic Algorithm, Particle Swarm Optimization, Error covariance matrix.

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