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Performance analysis of Kalman filter with Particle Swarm Optimization and fuzzy logic

To know the characteristics of a system, it is important to estimate the state of the system. State estimation can be done using stochastic estimators. Kalman filter is one of the linear stochastic estimators. Kalman filtering technique is used to track the target in several applications like radar applications, navigation systems, surveillance systems, communication systems, embedded applications etc. In this paper, we study about Kalman filter, Particle Swarm Optimization and fuzzy logic. In the presence of noise, the performance of Kalman filter is altered. Hence the noise power spectral densities are optimized using PSO and then filtering technique is used. For further improvement in the reduction of error in estimation, we analyze the incorporation of fuzzy logic to Kalman filter. The performance of Kalman filter and its combination with PSO and fuzzy logic is compared for tracking a maneuvering target.