Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment. Issue 12 (1st July 2021)

• Record Type: Journal Article
• Authors: Zhu, Bing; Li, Ding; Li, Zuohu; He, Hongyang; Li, Xing
• Is Part Of: IET radar, sonar & navigation. Volume 15:Issue 12(2021)
• Page Start: 1583
• Page End: 1593
• Publication Date: 2021-07-01
• Subjects: adaptive Kalman filters -- inertial navigation -- adaptive estimation -- measurement errors -- measurement uncertainty; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/rsn2.12148
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract: The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non‐Gaussian conditions. A robust strategy for adaptive estimation of R is proposed to suppress the influence of non‐Gaussian noise on the in‐motion alignment performance of the Doppler velocity log (DVL) velocity‐aided strapdown inertial navigation system (SINS). Furthermore, an improved Sage–Husa robust adaptive KF algorithm (SHRAKF) based on the Mahalanobis distance (MD) algorithm is proposed to handle the outliers that frequently occur within the complicated underwater environment. The contributions of this work are twofold—the SHRAKF (1) designs a robust strategy to adaptively estimate R in real time and (2) further improves filtering robustness and adaptability with the MD algorithm, conditional on the DVL outputs being contaminated by non‐Gaussian noise. A semi‐physical simulation experiment for SINS/DVL in‐motion alignment based on the test data is carried out, and the experimental results show that the SHRAKF adaptively estimates R in real time and effectively suppresses observational outliers. For non‐Gaussian noise pollution, including outliers and heavy‐tailed noise, the SHRAKF performs better than traditional methods.