Strapdown inertial navigation system alignment based on marginalised unscented Kalman filter

Chang, Lubin; Hu, Baiqing; Liu, An; Qin, Fangjun

doi:10.1049/iet-smt.2012.0071

Cited by 70 publications

(56 citation statements)

References 24 publications

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“…In this sense, when authors, as Cho et al [32,34], Xinlong [33], Chang et al [21], and many others [9,10,22,23,24,25,26,27,28,29,30,31], removed from their filters states other than the position errors, they seriously impaired their estimation performance. According to Savage [74], an important rule to bear in mind during the design process of Kalman filters is that error states to be included are those that significantly affect the measurement (i.e., error states that are “estimable”).…”

Section: Estimability Analysis and Simulation Resultsmentioning

confidence: 99%

“…When we examine how the error state selection (for SSAC purposes) has been performed in more recent works, we notice that Kalman filters with 15 states (full-order filter) [1,11,12], 12 states [13,14,15,16,17,18,19,20], 10 states (as in the Bar-Itzhack and Berman’s work) [10,21,22,23,24,25,26,27,28,29,30,31] and 9 states [32,33,34] are the most recurrent choices. Obvious questions that arise, in considering these works, are: Are these filters equivalent in terms of estimation performance?…”

Section: Introductionmentioning

confidence: 99%

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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: Estimability Analysis and Simulation Resultsmentioning

confidence: 99%

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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“…When IMU rotates about the Z axis, the R and R 2 matrix can be expressed as shown in equations (39) and (40), and the 1 st and 2 nd time derivatives of γ U are given in equations (41) and (42). (40)…”

Section: Imu Rotation About Z Axismentioning

confidence: 99%

Self‐alignment and calibration of a MEMS IMU: Improving observability and estimability by rotary motions of IMU

2019

Asian Journal of Control

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The stationary self-alignment and calibration (SSAC) for a low-cost MEMS IMU is quite challenging due to the poor observability of an inertial system under static condition and the significant sensor errors of MEMS inertial sensors. This research proposes to employ IMU rotations to improve the system observability and estimability regarding the SSAC of a low-cost MEMS IMU.IMU rotations about the X, Y, and Z axes are employed in this paper. The analytic estimation algorithm for each error state is derived and the observability of the system with IMU rotation is analyzed. As the observability analysis will not provide clues about how well an error state can be estimated, the estimability analysis is also conducted based on the eigenvalues and eigenvectors from the covariance matrix in the Kalman filter. Tests are conducted with a tri-axial turntable to verify the improvements on system observability and estimability brought by IMU rotations. Of both theoretical analysis and results indicated with proper IMU rotations, only azimuth error still remains unobservable, and the IMU rotation also significantly improves the estimability of all error states, including the unobservable azimuth.

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“…If the vibrations are so severe that the outputs of the gyroscopes are much larger than the rotational velocity of the Earth, AA cannot be performed. There are many approaches to the alignment problem including the inertial frame alignment method [7,8], Kalman fine alignment [9,10,11], and compass alignment [12,13]. All of these methods require part or all of the inertial navigation process, where the velocity and attitude of the SINS update with the time.…”

Section: Introductionmentioning

confidence: 99%

“…Because vibration is common in actual systems, INCA has been widely studied for several decades [7,8,9,10,12]. There have been few studies on AA, mainly because it has difficulty achieving alignment in the presence of vibrations.…”

Section: Introductionmentioning

confidence: 99%

A New Analytic Alignment Method for a SINS

Tan

Wang

et al. 2015

Sensors

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Analytic alignment is a type of self-alignment for a Strapdown inertial navigation system (SINS) that is based solely on two non-collinear vectors, which are the gravity and rotational velocity vectors of the Earth at a stationary base on the ground. The attitude of the SINS with respect to the Earth can be obtained directly using the TRIAD algorithm given two vector measurements. For a traditional analytic coarse alignment, all six outputs from the inertial measurement unit (IMU) are used to compute the attitude. In this study, a novel analytic alignment method called selective alignment is presented. This method uses only three outputs of the IMU and a few properties from the remaining outputs such as the sign and the approximate value to calculate the attitude. Simulations and experimental results demonstrate the validity of this method, and the precision of yaw is improved using the selective alignment method compared to the traditional analytic coarse alignment method in the vehicle experiment. The selective alignment principle provides an accurate relationship between the outputs and the attitude of the SINS relative to the Earth for a stationary base, and it is an extension of the TRIAD algorithm. The selective alignment approach has potential uses in applications such as self-alignment, fault detection, and self-calibration.

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Strapdown inertial navigation system alignment based on marginalised unscented Kalman filter

Cited by 70 publications

References 24 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

Self‐alignment and calibration of a MEMS IMU: Improving observability and estimability by rotary motions of IMU

A New Analytic Alignment Method for a SINS

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