Study of strapdown navigation attitude algorithms

Musoff, Howard; Murphy, James H.

doi:10.2514/3.21382

Cited by 31 publications

(15 citation statements)

References 2 publications

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“…Under static conditions, the time-delay problem does not affect the INS alignment results very much, whereas under mooring or voyaging conditions, as the INS are sometimes in the dynamic modes, errors caused by the time delay could accumulate gradually all through the alignment process [6]. This is will be validated and discussed in Section 6.…”

Section: Analyses Of Hmm-kfmentioning

confidence: 99%

“…Normally, alignment techniques can be categorized into two types based on the conditions of strapdown INS: stationary alignment and in-motion alignment [4–6]. The essence of stationary alignment is to compute the initial attitude matrix using two non-collinear vectors, namely the gravity and the earth rotational rate measurements from accelerometers and gyroscopes respectively [7,8].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Self-Alignment Method for Ship’s Strapdown INS Under Mooring Conditions

Sun

Lan

et al. 2013

Sensors

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Strapdown inertial navigation systems (INS) need an alignment process to determine the initial attitude matrix between the body frame and the navigation frame. The conventional alignment process is to compute the initial attitude matrix using the gravity and Earth rotational rate measurements. However, under mooring conditions, the inertial measurement unit (IMU) employed in a ship's strapdown INS often suffers from both the intrinsic sensor noise components and the external disturbance components caused by the motions of the sea waves and wind waves, so a rapid and precise alignment of a ship's strapdown INS without any auxiliary information is hard to achieve. A robust solution is given in this paper to solve this problem. The inertial frame based alignment method is utilized to adapt the mooring condition, most of the periodical low-frequency external disturbance components could be removed by the mathematical integration and averaging characteristic of this method. A novel prefilter named hidden Markov model based Kalman filter (HMM-KF) is proposed to remove the relatively high-frequency error components. Different from the digital filters, the HMM-KF barely cause time-delay problem. The turntable, mooring and sea experiments favorably validate the rapidness and accuracy of the proposed self-alignment method and the good de-noising performance of HMM-KF.

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Section: Analyses Of Hmm-kfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Self-Alignment Method for Ship’s Strapdown INS Under Mooring Conditions

Sun

Lan

et al. 2013

Sensors

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“…In 1971, Bortz [1] proposed the rotation vector concept as a means of obtaining a correction to be applied to the gyro outputs to account for noncommutativity effects. While the attitude updating algorithms have been evolved [2][3][4][5][6][7][8][9][10][11][12][13][14][15], very little parallel work has been published on the development of the companion SINS algorithms for velocity updating. Savage [16] provided a rigorous comprehensive approach to the design of the principal software velocity and position algorithms Manuscript utilized in modern-day SINS.…”

Section: Introductionmentioning

confidence: 99%

Optimal two-iteration sculling compensation mathematical framework for SINS velocity updating

Zhang

Chen

et al. 2014

J. of Syst. Eng. Electron.

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A new two-iteration sculling compensation mathematical framework is provided for modern-day strapdown inertial navigation system (SINS) algorithm design that utilizes a new concept in velocity updating. The principal structure of this framework includes twice sculling compensation procedure using incremental outputs from the inertial system sensors during the velocity updating interval. Then, the moderate algorithm is designed to update the velocity parameter. The analysis is conducted in the condition of sculling motion which indicates that the new mathematical framework error which is smaller than the conventional ones by at least two orders is far superior. Therefore, a summary is given for SINS software which can be designed with the new mathematical framework in velocity updating.

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“…To help the vehicles to complete the flight task or arrive at a desired destination, the strapdown systems which can meet the navigation requirements should be selected [7]. If an effective method that could be established to predict the velocity errors and positioning errors by assessing error parameters, a good deal of time for error analyses would be saved and the aircrafts would be more likely to accomplish the initial tasks [8,9]. Currently, some researchers have placed importance on the error analyses and error compensation of SINS [10].…”

Section: Introductionmentioning

confidence: 99%

Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM

Yin

Sun²,

Wang³

2013

Abstract and Applied Analysis

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The strapdown inertial navigation systems (SINS) have been widely used for many vehicles, such as commercial airplanes, Unmanned Aerial Vehicles (UAVs), and other types of aircrafts. In order to evaluate the navigation errors precisely and efficiently, a prediction method based on support vector machine (SVM) is proposed for positioning error assessment. Firstly, SINS error models that are used for error calculation are established considering several error resources with respect to inertial units. Secondly, flight paths for simulation are designed. Thirdly, the-SVR based prediction method is proposed to predict the positioning errors of navigation systems, and particle swarm optimization (PSO) is used for the SVM parameters optimization. Finally, 600 sets of error parameters of SINS are utilized to train the SVM model, which is used for the performance prediction of new navigation systems. By comparing the predicting results with the real errors, the latitudinal predicting accuracy is 92.73%, while the longitudinal predicting accuracy is 91.64%, and PSO is effective to increase the prediction accuracy compared with traditional SVM with fixed parameters. This method is also demonstrated to be effective for error prediction for an entire flight process. Moreover, the prediction method can save 75% of calculation time compared with analyses based on error models.

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Study of strapdown navigation attitude algorithms

Cited by 31 publications

References 2 publications

A Robust Self-Alignment Method for Ship’s Strapdown INS Under Mooring Conditions

A Robust Self-Alignment Method for Ship’s Strapdown INS Under Mooring Conditions

Optimal two-iteration sculling compensation mathematical framework for SINS velocity updating

Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM

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