Robust adaptive unscented Kalman filter and its application in initial alignment for body frame velocity aided strapdown inertial navigation system

Abstract

In the in-motion alignment of a strapdown inertial navigation system (SINS), the unscented Kalman filter (UKF) is usually used to solve non-linear problems. The measurement noise covariance R has a direct influence on the filtering results of the alignment of the SINS. The measurement noise is assumed to follow Gaussian distribution with a constant covariance R. However, these assumptions are often not realistic, neither the Gaussianity nor the constant covariance. This will degrade the performance of the UKF. To solve this problem, this paper proposes a novel adaptive robust UKF (NARUKF). In the NARUKF, a sliding window is used in estimating the covariance R in real-time. The NARUKF is divided into three main steps, the first step is to use the Mahalanobis distance algorithm to robustify the UKF. The second step is to use the projection statistics algorithm to reweight the abnormal stored innovations. Finally, the covariance R is adaptively estimated. The simulation and experimental results for the problem of the body frame velocity aided SINS in-motion alignment under heavier-tail distribution and/or outlier conditions demonstrate the superiority of the proposed method over the traditional ones.

Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment

“…To robustify the process of estimating R, an MD fromz k tô z kjk−1 is obtained as the judging index [11,24]; that is,…”

“…In the SINS/DVL dynamic alignment, the state vector is chosen as The symbol definitions of the state vector are shown in Table 1. According to the time update of KF and the SINS error equation [5,11], the expression of the state transition matrix F SINS is as follows:…”

“…However, because of the complicated underwater environmental characteristics, such as seabed geology, sea temperature, and salinity, the statistical characteristics of the DVL outputs (observations) may not agree with the Gaussian distribution. On the other hand, it also may be uncertain or unknown in practical application [11]. All of these will degrade the performance of SINS/DVL initial alignment based on the KF.…”

“…However, the performance of Xu's method deteriorates rapidly with outliers in the observations. Zhu et al [11] proposed a robust adaptive filtering method based on the projection statistics algorithm that improved the robustness and adaptability of the filtering process. The sliding time window used to store the innovation vectors plays an important role in Zhu's method.…”

Robust in‐field estimation and calibration approach for strapdown inertial navigation systems accelerometers bias acting on the vertical channel

“…In these situations, the performance of the KF estimator is greatly reduced especially in estimating the inertial sensor biases [3]. Some adaptive approaches are presented to estimate proper values for Q and R based on different disturbances and environment conditions [16–19]. However, the adaptive KF methods are model‐based approaches and have their own complexity in parameter tuning scheme with time‐consuming procedure.…”