Hybrid Particle Swarm Optimization and Unscented Filtering Technique for Estimation of Non-stationary Signal Parameters

This paper proposes an adaptive unscented Kalman filter for parameter estimation of non-stationary signals, like amplitude and frequency, in the presence of significant noise and harmonics. This paper proposes an iterative update equation for model and measurement error covariances Q and R to improve tracking of the filter in the presence of high noise. The initial choice of the model and measurement error covariances Q and R, along with the UKF parameters, are crucial in noise rejection. This paper utilizes a modified particle swarm optimization (MPSO) algorithm for the initial choice of the error covariances and UKF parameters. Various simulation results for time varying signals reveal significant improvement in noise rejection and accuracy in obtaining the frequency and amplitude of the signal.