Error analysis of analytical coarse alignment formulations for stationary SINS

Silva, Felipe O.; Hemerly, Elder Moreira; Filho, Waldemar C. Leite

doi:10.1109/taes.2016.7738355

Cited by 55 publications

(37 citation statements)

References 55 publications

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“…The coarse alignment is an analytical process that generates rough estimates of the vehicle initial orientation [6,7]. Due to some weak assumptions on which it is based, namely perfectly stationary conditions and unbiased inertial sensors, the coarse alignment generally fails to comply with the system’s accuracy requirements, and a posterior stochastic filtering and optimal estimation-based procedure becomes necessary [2].…”

Section: Introductionmentioning

confidence: 99%

On the Error State Selection for Stationary SINS Alignment and Calibration Kalman Filters—Part II: Observability/Estimability Analysis

Silva

Hemerly

Filho

2017

Sensors

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This paper presents the second part of a study aiming at the error state selection in Kalman filters applied to the stationary self-alignment and calibration (SSAC) problem of strapdown inertial navigation systems (SINS). The observability properties of the system are systematically investigated, and the number of unobservable modes is established. Through the analytical manipulation of the full SINS error model, the unobservable modes of the system are determined, and the SSAC error states (except the velocity errors) are proven to be individually unobservable. The estimability of the system is determined through the examination of the major diagonal terms of the covariance matrix and their eigenvalues/eigenvectors. Filter order reduction based on observability analysis is shown to be inadequate, and several misconceptions regarding SSAC observability and estimability deficiencies are removed. As the main contributions of this paper, we demonstrate that, except for the position errors, all error states can be minimally estimated in the SSAC problem and, hence, should not be removed from the filter. Corroborating the conclusions of the first part of this study, a 12-state Kalman filter is found to be the optimal error state selection for SSAC purposes. Results from simulated and experimental tests support the outlined conclusions.

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Section: Introductionmentioning

confidence: 99%

On the Error State Selection for Stationary SINS Alignment and Calibration Kalman Filters—Part II: Observability/Estimability Analysis

Silva

Hemerly

Filho

2017

Sensors

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“…Update K ( ) ,̂( ) and P ( ) / −1 : 10: (5,10,5,30,30) in the MSTKF method, (1) is diag (1,10,1,30,30), and (2) is diag (10,1,1,30,30) in the MSTSKF method. The simulation lasts about 600 s and the alignment results of KF, MSTKF, and MSTSKF are discussed and comprehensively evaluated.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The field experimental data is collected to evaluate the performance of the proposed MSTSKF method in large initial misalignment angles and mismatch of noise covariance matrix cases. In this experiment, the forgetting factor is 0.95 in both MSTKF method and MSTSKF method, the is diag (5,15,15,30,30) in the MSTKF method, (1) is diag(1,10,1,30,30), and (2) is diag (10,1,1,30,30) in the MSTSKF method.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…SINS can track the position and orientation of the movement of the carrier in real time based on the accelerometer and gyroscope inherent in SINS. The process of initial alignment prior to the navigation operation is of vital importance for SINS, the accuracy of subsequent dead-reckoning algorithm relies heavily on the initial attitude matrix, and a poor initial attitude matrix will result in much degradation and divergence for subsequent navigation [9,10].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

et al. 2019

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The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

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“…With the development of SINS, Silva et al derived attitude error formulations for stationary SINS [11]. The static model had a better application in research for its simple structure, but it did not apply to a dynamic base.…”

Section: Introductionmentioning

confidence: 99%

A General Euler Angle Error Model of Strapdown Inertial Navigation Systems

Dang

et al. 2018

Applied Sciences

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Featured Application: The proposed general Euler angle error model can intuitively describe the process of attitude motion. Especially in large-angle attitude movements, such as missile erection and manipulator control, real-time attitude information can be measured, after which the attitude motion can be optimized.Abstract: Attitude error models play an important role in analyzing the characteristics of navigation error propagation for the design and operation of strapdown inertial navigation systems (SINS). However, the majority of existing attitude error models focus on misalignment, rather than Euler angle errors. Misalignment cannot directly describe attitude error propagation, which is an indirect measurement. To solve the problem, a general Euler angle error model of SINS is proposed. Based on Euler angle error propagation analysis, relative Euler angle errors, and convected Euler angle errors are introduced to compose the general Euler angle error model. Simulation experiments are carried out to verify the proposed model.

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Error analysis of analytical coarse alignment formulations for stationary SINS

Cited by 55 publications

References 55 publications

On the Error State Selection for Stationary SINS Alignment and Calibration Kalman Filters—Part II: Observability/Estimability Analysis

On the Error State Selection for Stationary SINS Alignment and Calibration Kalman Filters—Part II: Observability/Estimability Analysis

An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

A General Euler Angle Error Model of Strapdown Inertial Navigation Systems

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