Transfer Alignment Design and Evaluation Environment

Jones, D. L.; Roberts, Chris; Tarrant, D.; Yang, Chun; Lin, Ching-Fang

doi:10.1109/aerocs.1993.721034

Cited by 16 publications

(9 citation statements)

References 8 publications

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“…Transfer alignment needs to use filtering technology to estimate online the S-SINS’s misalignment angles, and the attitude matrix can be effectively corrected at the end of alignment [12,13,14,15,16,17,18]. When the base is moving, there exist interference factors such as external noises or motion disturbances, which can decrease the accuracy of a S-SINS’s transfer alignment [19,20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

Lyu

Cheng

2017

Sensors

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Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.

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Section: Introductionmentioning

confidence: 99%

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

Lyu

Cheng

2017

Sensors

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“…[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] have achieved significant progress. The traditional and popular method used in TA technology is velocity match (VM).…”

Section: Introductionmentioning

confidence: 99%

“…Body flexure has been considered recently as different models of flexure phenomena have been used in rapid TA. [14][15][16][17] They generally add six additional states into the Kalman filter in order to define the angular flexures.…”

Section: Introductionmentioning

confidence: 99%

Novel Transfer Alignment of Shipborne Gimbaled Inertial Navigation Systems

Ding

Cai

Yang

2014

Trans. Japan Soc. Aero. S Sci.

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Aiming at transfer alignment of gimbaled inertial navigation systems (INS) on a moving base, we propose an attitude matching alignment model to align the attitude of the slave platform. This method is achieved by applying an unscented Kalman filter to estimate the fixed misalignment angle, and the misalignment angle can be obtained only with attitude maneuvers, which are very easy for the ship to implement. Firstly, the frame dynamics equations are introduced. Then, frame angular error differential equations, which include the ship-body flexure and other alignment errors, are set up via the frame angle information from the master and the slave INS platform. With these frameworks, the nonlinear attitude matching alignment model is designed based on unscented Kalman filter technology. The simulation results show that the proposed method can obtain an alignment to an accuracy of 1.5 0 and alignment time of 50 sections.

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“…It uses the accurate information of master INS as reference, and to estimates the errors between slave INS and master INS by Kalman filter (Titterton and Weston, 1990;Ohlmeyer et al, 2001;Ignagn, 1982). However, the real-time ability of Kalman filter cannot be achieved when a system's order is higher, as the computation time of Kalman filter is proportional to the cube of system's order ( Jones et al, 1993;Ignagni, 1990). Considering neural networks have the ability of self-learning and could approach any nonlinear function, and with rapid and collateral data processing capacity, thereby, they have been expected to replace Kalman filter to improve real-time performance of alignment and accuracy performance of INS.…”

Section: Introductionmentioning

confidence: 99%

Solution of transfer alignment problem of SINS on moving bases via neural networks

Wang

Shen

2011

Engineering Computations

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Purpose -In order to accomplish real-time alignment of Shipborne strapdown inertial navigation system (SINS) on moving bases, a novel solution method of utilizing neural networks for rapid transfer alignment of Shipborne SINS was investigated. Design/methodology/approach -The system error state equations and measurement equations of the Shipborne transfer alignment were established. Based on the nonlinear and time-variant SINS model on moving bases, a neural network learning algorithm based on Kalman filtering was presented, and the methods of constructing and training of neural networks input-output sample pairs suitable for Shipborne SINS were proposed. Findings -Velocity and attitude errors between the master and slave inertial navigation system (INS) are chosen as network's inputs, and the information of sample pairs is affluent, which can advance the stability and generalization of the neural networks. The neural networks algorithms based on Kalman filtering not only have the self-learning ability, but also remain recursive optimal estimation capability of Kalman filtering. Through the introducing of the local level trajectory frame, the trained neural networks can be independent on a ship heading, and only dependent on the relative position errors between master with slave INS and the inertial sensor errors. Originality/value -This article presents an innovative solution method of utilizing neural networks for rapid transfer alignment of Shipborne SINS.

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Transfer Alignment Design and Evaluation Environment

Cited by 16 publications

References 8 publications

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

Novel Transfer Alignment of Shipborne Gimbaled Inertial Navigation Systems

Solution of transfer alignment problem of SINS on moving bases via neural networks

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