A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

Wang, Di; Xu, Xiaosu; Yao, Yiqing; Zhang, Tao; Zhu, Yeyu

doi:10.1109/tim.2019.2955187

Cited by 102 publications

(29 citation statements)

References 22 publications

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“…Hence the dead reckoning (DR) technology is widely used to locate the AUVs, which is composed of the compass, the Doppler velocity log (DVL) and the pressure sensor [2]. Unfortunately, the localization accuracy of the DR method will diverge unboundedly with time due to the accumulation of measurement errors and lacks of external corrections [3].…”

Section: Applied Notationsmentioning

confidence: 99%

An Improved Variational Adaptive Kalman Filter for Cooperative Localization

Huang

Bai

et al. 2021

IEEE Sensors J.

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In this paper, an improved variational adaptive Kalman filter for cooperative localization with inaccurate prior information is proposed, in which the prior scale matrix of the one-step prediction error covariance matrix is adaptively estimated by using the expectation-maximization algorithm. A novel alternate iteration strategy is proposed to reduce the computational complexity of the proposed method. Convergence analysis and theoretical comparisons with the existing advanced adaptive Kalman filtering methods are also provided. Based on this, a new master-slave cooperative localization method is proposed. Lake experiment results of cooperative localization for autonomous underwater vehicles demonstrate the advantages of the proposed method over existing methods. Compared with the cutting-edge adaptive master-slave cooperative localization method, the proposed method has been improved by 22.52% in average localization error but no more than twice computational time is needed.

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Section: Applied Notationsmentioning

confidence: 99%

An Improved Variational Adaptive Kalman Filter for Cooperative Localization

Huang

Bai

et al. 2021

IEEE Sensors J.

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“…e physical parameters measured by gyroscopes and accelerometers of the SINS are used to calculate the trajectory of the carrier in the up direction when floating on the sea surface. e state equation is given as follows according to Wang et al [20]:…”

Section: Sins State Equationmentioning

confidence: 99%

A Novel Method to Estimate the Sea State for Recycling Work on the Sea Surface

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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The recycling of marine exploration equipment after it has surfaced is greatly affected by sea state. In order to estimate the sea state in real time, this paper proposes a method for measuring wave elevation, which modifies the integrated results of GNSS/SINS in the up direction by virtual horizontal lines to extract wave fluctuation information. From these wave information, the significant wave heights (SWH) can be calculated as the only input parameter of P-M spectrum, and a series of wave height data can be further simulated. When the GNSS is interrupted due to severe sea state, the simulated data can be integrated with the SINS to deal with the data distortion problem. The simulation results show that the application of wave spectrum in the GNSS intermittent situation has obvious improvement effect and important significance.

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“…where ϕ represents attitude error, δV represents velocity error, δλ, δL, δh denote position error, ε represents gyro bias, ∇ represents accelerometer bias, K D represents DVL scale factor, and b PS represents pressure sensor bias. e system state transition matrix F, the matrix G, and W can be expressed as follows [17]:…”

Section: State Equationmentioning

confidence: 99%

“…Taking DVL beam 1 as an example, its conversion relationship is shown in Figure 2. Figure 2 is reproduced from [17]. (under the creative commons attribution license/public domain).…”

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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A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

Cited by 102 publications

References 22 publications

An Improved Variational Adaptive Kalman Filter for Cooperative Localization

An Improved Variational Adaptive Kalman Filter for Cooperative Localization

A Novel Method to Estimate the Sea State for Recycling Work on the Sea Surface

An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

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