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, 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. Results show two presented method have less error.