Experimental Feasibility of the In-Drilling Alignment Method for Inertial Navigation in Measurement-While-Drilling

Jurkov, Alexander S.; Cloutier, Justin; Pecht, Efraim; Mintchev, Martin P.

doi:10.1109/tim.2010.2064631

Cited by 35 publications

(14 citation statements)

References 20 publications

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“…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%

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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show abstract

“…In ground static base alignment, with known velocity v n en (t) ≡ 0 substituting into (11), the proposed method in this paper can be transformed into the method in [18], which is essentially a special case of the proposed method in this paper.…”

Section: Mathematical Modeling and Algorithmmentioning

confidence: 99%

“…The predictive Kalman filter is used in the INS nonlinear alignment under large azimuth misalignment angle [10]. The INS navigation alignment method is used in in-drilling alignment [11]. Tightly coupling IMU/radio-frequency-identification integration is used in accurate pedestrian indoor navigation [12].…”

Section: Introductionmentioning

confidence: 99%

Quaternion-Optimization-Based In-Flight Alignment Approach for Airborne POS

Kang

Fang

Wang

2012

IEEE Trans. Instrum. Meas.

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Position and Orientation System (POS) is a key technology that provides motion compensation information for imaging sensor in airborne remote sensing. In-flight alignment (IFA) is an important alignment method that can improve reaction speed and accuracy of measurement for airborne POS. The traditional IFA methods based on the Kalman filter and the adaptive extended Kalman filter (AEKF) are severely affected by flight maneuver; severe maneuver may result in alignment accuracy degeneration or failure. On the basis of optimization-based alignment method for static base alignment, a novel IFA method based on quaternion optimization is devised in this paper, aiming to boost up the robustness and the accuracy of IFA, which extends the range of the application of optimization-based alignment method from static base alignment to IFA. The proposed algorithm has the advantage that it can avoid the affection from severe maneuver. The real-time implementation is given. Flight experiment demonstrates that, compared with the IFA method based on AEKF, the proposed algorithm is better in converging speed and robusticity with the same alignment accuracy. Index Terms-Flight maneuver, in-flight alignment (IFA), Position and Orientation System (POS), quaternion-constrained least square, robust, Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) integrated navigation system.

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“…Gyroscope-based system, which uses gyro and accelerometer, as well as a magnetometer-based system using magnetometer sensors and accelerometer sensors because, in the drilling engineering, the accelerometer is always used in various measuring systems [4,7].Continuous MWD is studied under laboratory conditions using a gyroscope-based system. Mahmoud et al [7,8] and Jurkov et al [9] proposed an advanced inclination and direction sensor package based on an inertial navigation system (INS). They verified the reliability of the algorithm through simulation, which used INS to achieve continuous MWD with high accuracy.…”

mentioning

confidence: 99%

“…Continuous MWD is studied under laboratory conditions using a gyroscope-based system. Mahmoud et al [7,8] and Jurkov et al [9] proposed an advanced inclination and direction sensor package based on an inertial navigation system (INS). They verified the reliability of the algorithm through simulation, which used INS to achieve continuous MWD with high accuracy.…”

mentioning

confidence: 99%

Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter

Yang

Gao

et al. 2020

Sensors

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In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory.

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Experimental Feasibility of the In-Drilling Alignment Method for Inertial Navigation in Measurement-While-Drilling

Cited by 35 publications

References 20 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

Quaternion-Optimization-Based In-Flight Alignment Approach for Airborne POS

Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter

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