Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chang, Yang Lang

doi:10.3390/s17010152

Cited by 20 publications

(19 citation statements)

References 17 publications

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“…The nominal sensor specifications of SINS in the simulation were set as the practical SINS, which are shown in Table 1. [18], CKF [14], the Sage-Husa adaptive Kalman filter (SHKF) [26], and CKF with true noise covariance matrices (TCKF) were selected to compare the performance with the proposed ACKF method. For the proposed ACKF, the tuning parameter, forgetting factor, and iteration number were set as τ = 5, ξ = 0.98, and N = 10, respectively.…”

Section: Simulationmentioning

confidence: 99%

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Zhang

Liu

2019

Sensors

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Initial alignment is critical and indispensable for the inertial navigation system (INS), which determines the initial attitude matrix between the reference navigation frame and the body frame. The conventional initial alignment methods based on the Kalman-like filter require an accurate noise covariance matrix of state and measurement to guarantee the high estimation accuracy. However, in a real-life practical environment, the uncertain noise covariance matrices are often induced by the motion of the carrier and external disturbance. To solve the problem of initial alignment with uncertain noise covariance matrices and a large initial misalignment angle in practical environment, an improved initial alignment method based on an adaptive cubature Kalman filter (ACKF) is proposed in this paper. By virtue of the idea of the variational Bayesian (VB) method, the system state, one step predicted error covariance matrix, and measurement noise covariance matrix of initial alignment are adaptively estimated together. Simulation and vehicle experiment results demonstrate that the proposed method can improve the accuracy of initial alignment compared with existing methods.

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Section: Simulationmentioning

confidence: 99%

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Zhang

Liu

2019

Sensors

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“…The adaptive alignment methods based on the Sage–Husa adaptive filter [ 17 ] and simplified Sage–Husa adaptive filter [ 18 ] were proposed, which could raise the efficiency and adaptability. Aiming at the dynamic disturbance, adaptive Kalman filter was developed, which could be used for the alignment of marine mooring rotary SINS [ 19 ] and airborne Micro-Electro-Mechanical-System (MEMS) SINS [ 20 ]. By combining nonlinear filters with adaptive estimation, some approaches based on adaptive nonlinear filters were proposed for SINS fine alignment under the conditions of large initial misalignment angles and unknown noise statistics, of which the adaptive nonlinear filters included the adaptive UKF (AUKF) [ 21 , 22 , 23 ], adaptive cubature Kalman filter (ACKF) [ 24 , 25 ], adaptive unscented particle filter (AUPF) [ 26 , 27 ], adaptive Lie group filter [ 28 ], etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Zhao

Yan

Yong-yuan

et al. 2020

Sensors

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In order to achieve the fine alignment of strapdown inertial navigation (SINS) under large misalignment angles, a novel filtering alignment method is proposed based on the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS). Firstly, the quaternion is employed to represent the attitude errors of SINS. A second-order nonlinear state equation is made based on the nonlinear velocity error model and attitude error model, and the linear measurement equation is based on the velocity outputs from SINS. Then, the filtering scheme is designed based on EKF2 and AFIS. The error estimation and fine alignment can be achieved by using the proposed filtering scheme. The results of Monte Carlo Simulation show that the errors of pitch, roll and yaw misalignment angles quickly decrease to about 14″, 15″ and 7.62′ respectively in 350 s under the condition of any misalignment angles with pitch error from −40° to 40°, roll error from −40° to 40°, and yaw error from −50° to 50°. Even when the initial misalignment angles are all very large such as (80°, 120°, 170°), the proposed nonlinear alignment method still can converge normally by utilizing the adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k−1. Finally, the turntable experiment was performed, and the effectiveness and superiority of the proposed method were further verified by compared with other nonlinear methods.

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“…To allow both swinging and static bases, a double-antenna method is adopted in [5], although this method is not suitable for use in bad weather. In [6], transmission alignment methods are adopted to avoid interference with GPS signals. However, the above methods are not suitable for selfalignment.…”

Section: Introductionmentioning

confidence: 99%

A Self-Alignment Method of MEMS Biaxial Rotation Modulation Strapdown Compass for Marine Applications

Huang

2019

IEEE Access

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Self-alignment of strapdown inertial navigation systems incorporating micro-electromechanical systems (MSINS) is a great challenge for marine applications. In this paper, a self-alignment method for a rotating MEMS strapdown compass is proposed with the aim of solving this problem. First, based on an analysis of biaxial rotation modulation and initial alignment of the strapdown compass, a selfalignment method is presented and verified. Second, by analyzing the effects of biaxial rotation modulation, the proposed method is improved by speeding up the rotation and reducing the stop time of the biaxial rotation mechanism to shorten the initial alignment time, which effectively suppresses the influence of MEMS noise on the initial alignment error angle. The influence of ship swinging on the initial alignment error angle is also analyzed. The efficiency of the method is verified by experiments on a swinging base. Finally, a parameter adjustment approach is presented that allows the proposed method to be used with different types of MEMS. This approach is validated by experiments. All the experimental results demonstrate the efficiency and precision of the proposed method. INDEX TERMS Biaxial rotation modulation, MEMS, self-alignment, strapdown compass. WEIQUAN HUANG received the B.S. degree in underwater acoustic electronics from the Harbin Shipbuilding Engineering Institute, in 1989, and the M.S. degree in information and signal processing and the Ph.D. degree in control theory and control engineering from Harbin Engineering University, in 1994 and 2006, respectively. He is currently a Professor with the College of Automation, Harbin Engineering University. His current research interests include underwater navigation, integrated navigation technology and new inertial devices, high-precision navigation systems, and MEMS inertial navigation systems. MENGHAO LI received the B.S. degree from the College of Automation, Harbin Engineering University, Harbin, China, in 2016, where he is currently pursuing the Ph.D. degree in control science and engineering. His current research interests include MEMS inertial navigation systems, MEMS biaxial rotation modulation strapdown inertial navigation systems, information fusion, and its application in MEMS integrated navigation systems.

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Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

Cited by 20 publications

References 17 publications

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

A Self-Alignment Method of MEMS Biaxial Rotation Modulation Strapdown Compass for Marine Applications

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