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.
Highlights
Unmanned Aerial Vehicles (UAVs), named drones, have attracted considerable attention because of their widespread use and potential threats
They were invented to perform military missions. They have taken over the consumer market with the development of technology. They play a crucial role in many areas, such as aerial photography, agriculture, plant protection, express delivery, disaster relief, wildlife observation, infectious-disease monitoring, mapping, news reports, power inspection, disaster relief, movie and television photography
Our work shows that when moving radars are applied to tracking UAVs, filtering random errors will not be accomplished well by traditional methods
Summary
Unmanned Aerial Vehicles (UAVs), named drones, have attracted considerable attention because of their widespread use and potential threats. This is a typical nonlinear problem while the Kalman filter is inappropriate to address this kind of issues. Their work featured a marginal computational cost, achieved the best possible final estimated state and reduced the convergence time They demonstrated their scheme capability by filtering simulated trajectories with low, medium, and high signal-to-noise ratios. A sequential converted measurement Kalman filter (SCMKF) with Doppler measurements in the ECEF coordinates was introduced in [18] It worked well for radar installed on a mobile airborne platform with time-varying attitude. We attempted to address the filter deviation problem by proposed extended nonlinear filtering algorithms based on ECEF coordinates.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
