An advanced error state Kalman filter (ESKF)-based terrain contour matching (TERCOM) method for tracking an aerial...

Area of Science:

• Robotics and Autonomous Systems
• Navigation and Control Systems
• Artificial Intelligence in Engineering

Background:

• Terrain Aided Navigation (TAN) is crucial for Uncrewed Aerial Vehicle (UAV) accuracy.
• Conventional TAN methods face limitations due to time-consuming map correlation, particularly with Digital Elevation Maps (DEMs).
• Computational complexity hinders real-time application of traditional TAN algorithms.

Purpose of the Study:

• To develop a computationally efficient Terrain Aided Navigation (TAN) algorithm for Uncrewed Aerial Vehicles (UAVs).
• To reduce the execution time and computational complexity of the map correlation process in TAN systems.
• To enhance the accuracy and suitability of TAN for real-time UAV navigation.

Main Methods:

• A fuzzy heuristic method for the mean absolute deviation (MAD) correlation scheme (FH-MAD) was developed.
• Fuzzy logic utilizes onboard sensor data (heading, roll) to identify matching map areas.
• An error state Kalman Filter (ESKF) was integrated for position estimation during maneuvers.

Main Results:

• The FH-MAD method demonstrated a significant reduction in computation time compared to conventional TAN techniques.
• Improved position accuracy was observed across tests using diverse Digital Elevation Maps (DEMs).
• The proposed system proved effective in various topographical conditions and UAV maneuvering scenarios.

Conclusions:

• The FH-MAD algorithm offers a computationally efficient and accurate solution for TAN in UAVs.
• The integration of fuzzy logic and ESKF enhances real-time navigation capabilities.
• This approach is well-suited for practical, real-time UAV navigation applications demanding high accuracy and speed.