An advanced error state Kalman filter (ESKF)-based terrain contour matching (TERCOM) method for tracking an aerial vehicle using a low-cost digital elevation map

Terrain Aided Navigation (TAN) systems hold significant potential for delivering accurate navigation for Uncrewed Aerial Vehicles (UAVs). However, a major limitation of conventional TAN systems lies in the time-consuming correlation technique used to search the <i>a priori</i> map, specifically the Digital Elevation Maps (DEM). This article presents a fuzzy heuristic method for the mean absolute deviation (MAD) correlation scheme (FH-MAD), aimed at reducing the computational complexity and execution time of the TAN algorithm. The fuzzy logic system uses heading and roll angle data from onboard sensors to determine the aircraft's matching area. The output membership functions are designed based on parameters that depend on terrain features. Additionally, the proposed method incorporates an error state Kalman Filter (ESKF) as the navigation algorithm to estimate the UAV's position under various maneuvering conditions. To evaluate the effectiveness of the proposed system, tests were conducted using two distinct DEMs with varying topographical characteristics and dimensions. The results demonstrate improved position accuracy and a significant reduction in computation time compared to traditional TAN methods, making the approach suitable for real-time UAV navigation applications.