PARAMETER-LESS SIMULATED KALMAN FILTER

Abstract

Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. In the original SKF algorithm, three parameter values are assigned during initialization, the initial error covariance, P(0), the process noise, Q, and the measurement noise, R. Further studies on the effect of P(0), Q and R values suggest that the SKF algorithm can be realized as a parameter-less algorithm. Instead of using constant values suggested for the parameters, this study uses random values for all three parameters, P(0), Q and R. Experimental results show that the parameter-less SKF managed to converge to near-optimal solution and performs as good as the original SKF algorithm.