A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

Jin, Kaidi; Chai, Hongzhou; Su, Chuhan; Xiang, Ming

doi:10.1088/1361-6501/acd9e1

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…For filtering algorithms, accuracy, operational speed, and adaptive adjustment ability are particularly important indicators. Jin et al [11] proposed a novel data-driven approach enhancing a DVL/SINS integrated navigation system, by building a virtual beam predictor based on multi-output least-squares support vector regression (MLS-SVR), to improve the robustness and accuracy of UUV navigation with limited DVL beams. Zhang et al [12] proposed a long short-term memory extended exponential weighted Kalman filter (LSTM-EEWKF) algorithm assisted by a long short-term memory (LSTM) neural network.…”

Section: Dead Reckoningmentioning

confidence: 99%

A Low-Cost and High-Precision Underwater Integrated Navigation System

Liu,

Yu,

et al. 2024

JMSE

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The traditional underwater integrated navigation system is based on an optical fiber gyroscope and Doppler Velocity Log, which is high-precision but also expensive, heavy, bulky and difficult to adapt to the development requirements of AUV swarm, intelligence and miniaturization. This paper proposes a low-cost, light-weight, small-volume and low-computation underwater integrated navigation system based on MEMS IMU/DVL/USBL. First, according to the motion formula of AUV, a five-dimensional state equation of the system was established, whose dimension was far less than that of the traditional. Second, the main source of error was considered. As the velocity observation value of the system, the velocity measured by DVL eliminated the scale error and lever arm error. As the position observation value of the system, the position measured by USBL eliminated the lever arm error. Third, to solve the issue of inconsistent observation frequencies between DVL and USBL, a sequential filter was proposed to update the extended Kalman filter. Finally, through selecting the sensor equipment and conducting two lake experiments with total voyages of 5.02 km and 3.2 km, respectively, the correctness and practicality of the system were confirmed by the results. By comparing the output of the integrated navigation system and the data of RTK GPS, the average position error was 4.12 m, the maximum position error was 8.53 m, the average velocity error was 0.027 m/s and the average yaw error was 1.41°, whose precision is as high as that of an optical fiber gyroscope and Doppler Velocity Log integrated navigation system, but the price is less than half of that. The experimental results show that the proposed underwater integrated navigation system could realize the high-precision and long-term navigation of AUV in the designated area, which had great potential for both military and civilian applications.

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Section: Dead Reckoningmentioning

confidence: 99%

A Low-Cost and High-Precision Underwater Integrated Navigation System

Liu,

Yu,

et al. 2024

JMSE

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“…In recent years, with the rapid development of artificial intelligence algorithms, its applicability in solving nonlinear problems has gradually improved, so it has begun to be used to assist navigation systems. Researchers have used methods such as support vector regression (SVR) [29], extreme learning machine (ELM) [30], Back propagation (BP) network [31], Long Short-Term Memory (LSTM) network [32], gated recurrent unit (GRU) network [33] and LightGBM regression [34] to conduct research on navigation assistance for vehicles, ships, and unmanned underwater vehicles. Meanwhile, when using artificial intelligence algorithms to predict GNSS navigation results using INS navigation results, historical data based on a certain step size can effectively improve the prediction accuracy [35].…”

Section: Introductionmentioning

confidence: 99%

A robust integrated navigation optimization method for USV in signal occlusion environment

Lou,

Liu,

et al. 2024

Phys. Scr.

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Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

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In-motion fine alignment algorithm for AUV based on improved extended state observer and Kalman filter

Ma,

Zhou,

Wang

et al. 2024

Meas. Sci. Technol.

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Achieving fast initial alignment has always been a major challenge for autonomous underwater vehicle navigation, especially in motion. During the fine alignment process, traditional algorithms often face the slow convergence of the azimuth misalignment angle. To solve this problem, this paper proposes a fast alignment algorithm based on the improved extended state observer (ESO) and Kalman filter. Specifically, since the application potential of ESO in fine alignment has not been fully explored by previous studies, this paper introduces the improved ESO through the in-depth research of traditional ESO. The obtained improved ESO is then combined with Kalman filter to constitute the proposed fast alignment algorithm. The algorithm first uses Kalman filter to estimate the two horizontal misalignment angles. Once the horizontal angles estimation of Kalman filter tends to be stable, the improved ESO is used to estimate the azimuth misalignment angle. Simulation and lake test show the proposed algorithm has excellent performance of in-motion alignment. The comparative analysis shows that the algorithm significantly improves the convergence speed of azimuth alignment angle.

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A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

Cited by 5 publications

References 28 publications

A Low-Cost and High-Precision Underwater Integrated Navigation System

A Low-Cost and High-Precision Underwater Integrated Navigation System

A robust integrated navigation optimization method for USV in signal occlusion environment

In-motion fine alignment algorithm for AUV based on improved extended state observer and Kalman filter

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