Sensor Bias Error Estimation for Terrain-Aided Navigation using Low-Cost AUVs

Particle filtering and Kalman filtering have been commonly used in terrain-aided navigation applications for autonomous underwater vehicles (AUVs) where digital terrain maps are available and bottom terrains are sufficiently varied. To the author’s knowledge, the reported literature on TAN using particle / Kalman filtering has not explicitly considered estimating the navigation sensor bias errors. Having good estimates of these errors can be used to improve the vehicle positioning performance and compensate for other payload sensor data (such as sonars) that might otherwise be affected. This paper investigates the performance of using an error-state, extended Kalman filter (ES-EKF) for terrain-aided navigation for low-cost AUVs with a Doppler-Velocity-Log and a magnetic compass so that the vehicle position, sensor biases, and ocean currents can be simultaneously estimated. The preliminary results based on Monte-Carlo runs suggest that ES-EKF can effectively estimate the vehicle position (< 130m), the sensor biases, and ocean currents (< 0.3m/s).