Robust SCKF Filtering Method for MINS/GPS In-Motion Alignment

Abstract

This paper presents a novel multiple strong tracking adaptive square-root cubature Kalman filter (MSTASCKF) based on the frame of the Sage–Husa filter, employing the multi-fading factor which could automatically adjust the Q value according to the rapidly changing noise in the flight process. This filter can estimate the system noise in real-time during the filtering process and adjust the system noise variance matrix Q so that the filtering accuracy is not significantly reduced with the noise. At the same time, the residual error in the filtering process is used as a measure of the filtering effect, and a multiple fading factor is introduced to adjust the posterior error variance matrix in the filtering process, so that the residual error is always orthogonal and the stability of the filtering is maintained. Finally, a vibration test is designed which simulates the random noise of the short-range guided weapon in flight through the shaking table and adds the noise to the present simulation trajectory for semi-physical simulation. The simulation results show that the proposed filter can significantly reduce the attitude estimation error caused by random vibration.

Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

“…Additionally, the clock states of the receiver were predicted using the receiver's clock bias state-space model. The predicted covariance matrix was obtained by Equation (20).…”

“…These methods are related to the observability rank of the error model of the system. Several in-flight alignment methods of INS/GNSS integration systems have also been investigated in [19,20]. The articles could correct the orientation results and the INS errors, which may be caused by misalignment or vibrations.…”

A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters

“…Among them, the Kalman filter represents a very appealing choice [29][30][31]. As compared to other adaptive filters, where the system to be identified is considered to be deterministic in their derivations, the Kalman filter takes the "uncertainties" in the system into account, and is thus successfully employed in a wide range of applications, e.g., [32][33][34][35][36][37] and the references therein. Recently, in [36], an adaptive Kalman-filter-based variational Bayesian, which achieves a simultaneous estimation of the process noise covariance matrix and of the measurement noise covariance matrix, is presented, with applications in target tracking.…”

“…Recently, in [36], an adaptive Kalman-filter-based variational Bayesian, which achieves a simultaneous estimation of the process noise covariance matrix and of the measurement noise covariance matrix, is presented, with applications in target tracking. In [37], the authors propose a multiple strong tracking adaptive square-root cubature Kalman filter, which can be used to improve the in-flight alignment, with applications in guided weaponry, unmanned automatic vehicles, and robots. The numerous different fields of applicability of the Kalman filter represent the main motivation behind the development presented in this paper, which targets the derivation of such a filter, tailored to the identification of multilinear forms.…”