Adaptive UT-H∞ Filter for SINS’ Transfer Alignment Under Uncertain Disturbances

Weiwei Lyu,Jinling Wang,Xianghong Cheng

doi:10.1109/access.2020.2986715

Weiwei Lyu, Jinling Wang + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2986715

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Abstract

Accurate and rapid transfer alignment with large attitude errors under uncertain disturbances is crucial for the strapdown inertial navigation system (SINS). This paper proposes an adaptive UT-Hoc filter which combines UKF technology and a Hoc filter to increase the robustness of the nonlinear transfer alignment system. By focusing on the time-varying and the uncertain external disturbances, the robustness factor of the adaptive UT-H∞ filter can be adaptively adjusted to balance the robustness and filtering accuracy of the dynamic system. Then, the nonlinear error propagation model of the transfer alignment is established in detail, and the velocity plus attitude matrix measurement model is used to improve the performance of transfer alignment. Moreover, the sensor error compensation model is established to calibrate and compensate for the sensor errors of the gyros and accelerometers online during transfer alignment. The vehicle transfer alignment experiments show that the proposed adaptive UT-Hoc filter can significantly improve the transfer alignment accuracy and the pure inertial navigation accuracy compared with the existing filtering methods under uncertain disturbances.

Highlights

A strapdown inertial navigation system (SINS) is an allweather independent navigation system which can provide accurate three-dimensional information on attitude, velocity and position of a vehicle [1], [2]
This paper proposes an adaptive UT-H∞ filter which can dynamically adjust the value of ξ under uncertain external disturbances
In this work, an adaptive UT-H∞ filter is proposed for SINS transfer alignment with large attitude errors under uncertain disturbances

Summary

Introduction

A strapdown inertial navigation system (SINS) is an allweather independent navigation system which can provide accurate three-dimensional information on attitude, velocity and position of a vehicle [1], [2]. As the performance of the SINS is greatly influenced by the accuracy and rapidness of the initial alignment [3]–[5], it is crucial to study the initial alignment methods when the SINS is used in different application scenarios. The initial alignment can be classified into self-alignment, transfer alignment, and combination alignment [6]. Compared with the other two alignment modes, transfer alignment has two significant advantages. One is that transfer alignment is more rapid, so it can be completed in a shorter time [7]. The other is that self-alignment and combination alignment require high sensor accuracy [3], [4], [6]; when the SINS has low sensor

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Results

Conclusion