An interval Kalman filter-based fuzzy multi-sensor fusion approach for fault-tolerant heading estimation of an autonomous surface vehicle

作者：Motwani Amit; Liu Wenwen; Sharma Sanjay; Sutton Robert; Bucknall Richard

来源：Proceedings of the Institution of Mechanical Engineers - Part M: Journal of Engineering for the Maritime Environment , 2016, 230(3): 491-507.

DOI：10.1177/1475090215596180

• Multi-sensor data fusion
• interval Kalman filtering
• fuzzy logic
• autonomous surface vehicle

This article presents a novel fuzzy-logic based multi-sensor data fusion algorithm for combining heading estimates from three separate weighted interval Kalman filters to construct a robust, fault-tolerant heading estimator for the navigation of the Springer autonomous surface vehicle. A single, low-cost gyroscopic unit and three independent compasses are used to acquire data onboard the vehicle. The gyroscope data, prone to sporadic bias drifts, are fused individually with readings from each of the compasses via a weighted interval Kalman filter. Unlike the standard Kalman filter, the weighted interval Kalman filter is able to provide a robust heading estimate even when subject to such gyroscope bias drifts. The three ensuing weighted interval Kalman filter estimates of the vehicle's heading are then fused via a fuzzy logic algorithm designed to provide an accurate heading estimate even when two of the three compasses develop a fault at any time. Simulations and real-time trials demonstrate the effectiveness of the proposed method.

• 出版日期2016-8