Ensemble Kalman Filter for Track-Before-Detect Algorithm of Pulsed Doppler Radar

Abstract

The purpose of this paper is to show the effectiveness of Ensemble Kalman filter (EnKF) for Pulsed Doppler radar under Track-Before-Detect (TBD) environments (Heavy Clutter Environments). To do this, we designed the Pulsed Doppler radar simulator for tracking a virtual target. This simulator includes the whole process of radar signal processing. In order to consider the nonlinearity of measurement data and TBD environment, we lowered the CFAR threshold to generate many false alarms and we consider a complicated moving path of a target. Under these conditions, the tracking performances of EnKF, Extended Kalman Filter, and Particle Filter (PF) are compared and analyzed. In this given scenario, the performances of EnKF and PF were more reliable than EKF. EnKF and PF showed similar performances. Considering complexity and diversity loss by resampling that PF requires, this result shows that EnKF can be an effective alternative of PF, because the optimization of EnKF is simpler than PF.