Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking

Abstract

Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation, which is offline. Tuning is the process of estimating the process noise covariance matrix (Q) and measurement noise covariance matrix (R) of the filter. This paper presents tuning of UKF using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for ballistic target tracking. Simulations results show that the superiority of PSO tuned UKF over conventional UKF.