A Weighted Measurement Fusion Kalman Filter implementation for UAV navigation

A Weighted Measurement Fusion Kalman Filter (WMFKF) is proposed for unmanned aerial vehicle (UAV) navigation. The measurement error of wireless localization sensors depends on the traveling distance, multipath effects, and sensor noise. In the proposed WMFKF fusion process, each measurement is weighted based on the signal traveling distance. The WMFKF estimation performance is compared to the standard KF in two scenarios. The first scenario assumes using a wireless local positioning system (WLPS) in a GPS-denied environment. The second scenario assumes the availability of both WLPS and GPS measurements. The simulation results show that when the detection range is only 10 km, both the WMFKF and standard Kalman Filter (KF) fail to converge their position estimation error within the three sigma boundaries in the GPS denied environment. However, the WMFKF maintains the position estimation error within its expected error boundary when the WLPS detection range limit is above 30 km. The WMFKF has a better accuracy whether GPS is available or GPS is denied when the detection range limit is above 30 km. The computational cost analysis shows that the WMFKF has less computational complexity than the standard KF. The WMFKF has a higher ellipsoid error probable percentage than the standard Measurement Fusion method.