A high accuracy multiplex two-position alignment method based on SINS with the aid of star sensor

Wang, Xinlong; Guan, Xujun; Fang, Jiancheng; Li, Hengnian

doi:10.1016/j.ast.2014.11.022

Cited by 23 publications

(10 citation statements)

References 9 publications

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“…Remark 1. In most previous results, such as [5,6,37], stochastic error of inertial sensor is simplified to be a random constant. In this article, we adopt a first-order Markov process (15) to represent the stochastic error of inertial sensor, which can approximate the physical random processes with improved accuracy.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…In addition, the modeling error and environmental disturbance of SINS are merged into the norm bounded disturbance d(t) rather than a Gaussian noise. Different from [5,6,15,16], performance degradation or faults of the inertial sensors are further discussed to improve the reliability of INS in this article.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…Analytic approaches are often applied to the coarse alignment. The most widely used fine alignment method is based on Kalman filter (KF) (see [4,5]). Kalman filtering, known as linear quadratic estimation (LQE), is an algorithm that uses Bayesian inference and estimates a joint probability distribution over the variables for each time frame.…”

Section: Introductionmentioning

confidence: 99%

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Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Cao

Guo

Chen

2018

Aerospace Science and Technology

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Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs subjected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability.

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Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Cao

Guo

Chen

2018

Aerospace Science and Technology

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“…They are the most accurate optical attitude sensors, at present [1]. Due to their high navigation accuracy, strong autonomy, and lack of cumulative error, they are favored in the aerospace industry [2,3]. As the “eyes” of the spacecraft, the accuracy of the star sensor determines the performance of the spacecraft directly.…”

Section: Introductionmentioning

confidence: 99%

A Star Sensor On-Orbit Calibration Method Based on Singular Value Decomposition

Heikkilä

et al. 2019

Sensors

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The navigation accuracy of a star sensor depends on the estimation accuracy of its optical parameters, and so, the parameters should be updated in real time to obtain the best performance. Current on-orbit calibration methods for star sensors mainly rely on the angular distance between stars, and few studies have been devoted to seeking new calibration references. In this paper, an on-orbit calibration method using singular values as the calibration reference is introduced and studied. Firstly, the camera model of the star sensor is presented. Then, on the basis of the invariance of the singular values under coordinate transformation, an on-orbit calibration method based on the singular-value decomposition (SVD) method is proposed. By means of observability analysis, an optimal model of the star combinations for calibration is explored. According to the physical interpretation of the singular-value decomposition of the star vector matrix, the singular-value selection for calibration is discussed. Finally, to demonstrate the performance of the SVD method, simulation calibrations are conducted by both the SVD method and the conventional angular distance-based method. The results show that the accuracy and convergence speed of both methods are similar; however, the computational cost of the SVD method is heavily reduced. Furthermore, a field experiment is conducted to verify the feasibility of the SVD method. Therefore, the SVD method performs well in the calibration of star sensors, and in particular, it is suitable for star sensors with limited computing resources.

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“…Using the inertial attitude matrix from the star sensor as the reference information, the attitude error of SINS which increases with time can be corrected. In the application of initial alignment, a multiplex two-position alignment method with the aid of star sensor is proposed in [ 18 ], which can meet the high accuracy requirements. Star sensors are highly accurate attitude sensitive instruments, widely used for attitude determination [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A New Polar Transfer Alignment Algorithm with the Aid of a Star Sensor and Based on an Adaptive Unscented Kalman Filter

Cheng¹,

Wang²,

Wang³

et al. 2017

Sensors

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Because of the harsh polar environment, the master strapdown inertial navigation system (SINS) has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA) algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF) is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.

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A high accuracy multiplex two-position alignment method based on SINS with the aid of star sensor

Cited by 23 publications

References 9 publications

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

A Star Sensor On-Orbit Calibration Method Based on Singular Value Decomposition

A New Polar Transfer Alignment Algorithm with the Aid of a Star Sensor and Based on an Adaptive Unscented Kalman Filter

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