A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

Wang, Di; Xu, Xiaosu; Yao, Yiqing; Zhu, Yeyu; Tong, Jinwu

doi:10.1109/access.2019.2924593

Cited by 21 publications

(6 citation statements)

References 27 publications

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“…where H PS represents the true value,δb PS represents the PS biases, and w PS represents the white noise. e DVL measurement error model can be expressed as [18] V…”

Section: Measurement Equationmentioning

confidence: 99%

An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

Wang

Hou

2020

Mathematical Problems in Engineering

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The main challenge of Strap-down Inertial Navigation System (SINS)/Doppler velocity log (DVL) navigation system is the external measurement noise. Although the Sage–Husa adaptive Kalman filter (SHAKF) has been introduced in the integrated navigation field, the precision and stability of the SHAKF are still the tricky problems to be overcome. The primary aim of this paper is to improve the precision and stability of underwater SINS/DVL system. To attain this, a SINS/DVL tightly integrated model is established, where beam measurements are used without transforming them to 3D velocity. The proposed improved SHAKF algorithm is based on variable sliding window estimation and fading filter. The simulations and vehicle test results demonstrate the effectiveness of the proposed underwater SINS/DVL tightly integrated navigation method based on the improved SHAKF. In addition, the position accuracy of the designed method outperforms that of the SHAKF method.

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“…where H PS represents the true value,δb PS represents the PS biases, and w PS represents the white noise. e DVL measurement error model can be expressed as [18] V…”

Section: Measurement Equationmentioning

confidence: 99%

An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

Wang

Hou

2020

Mathematical Problems in Engineering

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“…Many researchers have deeply studied the information fusion method of multi-sensor integrated navigation system. [1][2][3] The research works are focused on synchronous fusion which considers that the data output rates of each auxiliary navigation sensor are same [4][5][6] and are transmitted synchronously to the fusion center. 7,8 However, due to the performance of auxiliary navigation sensors and other conditions in actual situation, the auxiliary navigation sensors may have different data output rates, which will make the multi-sensor information asynchronously arrive in the fusion center.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale asynchronous fusion algorithm for multi-sensor integrated navigation system

Xueyuan

Pan²,

Sun³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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The asynchronous fusion method is the key technology of multi-sensor integrated navigation system which is independently observed by multiple auxiliary navigation sensors with different data output rates. The multi-scale asynchronous fusion can effectively increase the accuracy of integrated navigation system, but the existing multi-scale asynchronous fusion algorithm has the characteristic of heavy computational burden and requires the data output rates of auxiliary navigation sensors to meet unrealistic special requirements. To solve this problem, a multi-scale asynchronous fusion algorithm for the multi-sensor integrated navigation system is presented based on state block vector and wavelet transform. This algorithm first converts the system’s original state equation into the relationship between the state block vector and the state point vector to obtain the state equation of the multi-scale dynamic system, and expresses the original measurement equation as the relationship with the state block vector to obtain the measurement equation of the multi-scale dynamic system. Then, the special implementation process of this algorithm is studied and expressed in details, on the basis of the matrix operator with scale and wavelet property. Finally, a multi-sensor integrated navigation system composed of strapdown inertial navigation system, celestial navigation system, global navigation satellite system, and air data system is designed and experimented validation analysis. Experiment results show that this algorithm can significantly reduce the average root mean square error and improve the accuracy of navigation parameters, compared with asynchronous preprocessing Kalman filter and asynchronous direct Kalman filter. It would have a wide prospect in multi-sensor integrated navigation systems.

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“…Among observer methods, Kalman Filter (KF), a representative method is used to predict hydrodynamic coefficients and state variables. 2 Currently, the Kalman filter which integrated INS and aiding devices [10][11][12][13] is the most used information fusion technique for integrated navigational system of AUV.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al 13 proposed a hybrid approach for an integrated navigational system of INS/DVL mainly under long-term mission conditions. In this approach, velocity errors of DVL during swing of the vehicle was compensated using error model based on motion attitude assist and a real-time filter based on an improved auto regressive model provided gyroscope signal de-noising.…”

Section: Introductionmentioning

confidence: 99%

Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

Dinc

Hajiyev

2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.

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A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

Cited by 21 publications

References 27 publications

An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

Multi-scale asynchronous fusion algorithm for multi-sensor integrated navigation system

Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

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