Improved Kalman Filter Variants for UAV Tracking with Radar Motion Models

Abstract

Unmanned aerial vehicles (UAV) have made a huge influence on our everyday life with maturity of technology and more extensive applications. Tracking UAVs has become more and more significant because of not only their beneficial location-based service, but also their potential threats. UAVs are low-altitude, slow-speed, and small targets, which makes it possible to track them with mobile radars, such as vehicle radars and UAVs with radars. Kalman filter and its variant algorithms are widely used to extract useful trajectory information from data mixed with noise. Applying those filter algorithms in east-north-up (ENU) coordinates with mobile radars causes filter performance degradation. To improve this, we made a derivation on the motion-model consistency of mobile radar with constant velocity. Then, extending common filter algorithms into earth-centered earth-fixed (ECEF) coordinates to filter out random errors is proposed. The theory analysis and simulation shows that the improved algorithms provide more efficiency and compatibility in mobile radar scenes.