Vehicle platform attitude estimation method based on adaptive Kalman filter and sliding window least squares

Abstract

Precision instrument measurement on an unstable platform is a difficult engineering problem, and the commonly used method is to compensate the instrument measurement results through platform attitude estimation. This paper derives a three-dimensional attitude estimation method based on inertial sensors including gyroscope and inclinometer. In order to deal with the inertial sensor noise, low-pass filter, Kalman filter, adaptive Kalman filter (AKF) and sliding window least squares (SWLS) are chosen to test the filtering performance from the frequency domain perspective. Practical filtering experiments indicate that AKF achieves the best filtering performance for gyroscope, while SWLS has the best filtering performance for inclinometer. Using AKF and SWLS to deal with inertial sensor outputs, the attitude estimation of the vehicle platform is realized. The proposed method is verified on vehicle-mounted electro-optical measurement equipment.