Discrete-Time Complementary Filters for Attitude and Position Estimation: Design, Analysis and Experimental Validation

This paper develops a navigation system based on complementary filtering for position and attitude estimation, with application to autonomous surface crafts. Using strapdown inertial measurements, vector observations, and global positioning system (GPS) aiding, the proposed complementary filters provide attitude estimates in Euler angles representation and position estimates in Earth frame coordinates, while compensating for rate gyro bias. Stability and performance properties of the proposed filters under operating conditions usually found in oceanic applications are derived, and the tuning of the filter parameters in the frequency domain is emphasized. The small computational requirements of the proposed navigation system make it suitable for implementation on low-power hardware and using low-cost sensors, providing a simple yet effective multirate architecture suitable to be used in applications with autonomous vehicles. Experimental results obtained in real time with an implementation of the proposed algorithm running on-board the DELFIMx catamaran, an autonomous surface craft developed at ISR/IST for automatic marine data acquisition, are presented and discussed.