Autonomious Undetrwater Vehicles Navigation and Localization Systems: A Survey

Es-sadaoui, Redouane; Guermoud, Mohammed; Khallaayoune, Jamal; Brizard, Tamara

doi:10.2139/ssrn.3186325

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…UWAR systems are used also for recreative applications such as underwater exploration of archaeological underwater parks [ 2 , 3 ]. In order to improve those tasks, accurate localization and navigation become extremely important to guarantee the correctness of the gathered data for these applications [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate Localization in Acoustic Underwater Localization Systems

Cario

Casavola

Gagliardi

et al. 2021

Sensors

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In underwater localization systems several sources of error may impact in different ways the accuracy of the final position estimates. Through simulations and statistical analysis it is possible to identify and characterize such sources of error and their relative importance. This is especially of use when an accurate localization system has to be designed within required accuracy prescriptions. This approach allows one to also investigate how much these sources of error influence the final position estimates achieved by an Extended Kalman Filter (EKF). This paper presents the results of experiments designed in a virtual environment used to simulate real acoustic underwater localization systems. The paper intends to analyze the main parameters that significantly influence the position estimates achieved by a Short Baseline (SBL) system. Specifically, the results of this analysis are presented for a proprietary localization system constituted by a surface platform equipped with four acoustic transducers used for the localization of an underwater target. The simulator here presented has the purpose of simulating the hardware system and modifying some of its design parameters, such as the base-line length and the errors on the GPS and Inertial Measurement Unit (IMU) units, in order to understand which parameters have to modify for improving the accuracy of the entire positioning system. It is shown that statistical analysis techniques can be of help in determining the best values of these parameters that permit to improve the performance of a real hardware system.

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Section: Introductionmentioning

confidence: 99%

Accurate Localization in Acoustic Underwater Localization Systems

Cario

Casavola

Gagliardi

et al. 2021

Sensors

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Smart Autonomous Moving Platforms

Guizani

Ghoneim

et al. 2020

IEEE Network

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Improving the underwater navigation performance of an IMU with acoustic long baseline calibration

Wu,

Nie,

Liu

et al. 2024

Satell Navig

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Underwater acoustic Long-Baseline System (LBL) is an important technique for submarine positioning and navigation. However, the high cost of the seafloor equipment and complex construction of a seafloor network restrict the distribution of the LBL within a small area, making an underwater vehicle difficult for long-distance and high-precision acoustic-based or inertial-based navigation. We therefore propose an acoustic LBL-based Inertial Measurement Unit (IMU) calibration algorithm. When the underwater vehicle can receive the acoustic signal from a seafloor beacon, the IMU is precisely calibrated to reduce the cumulative error of Strapdown Inertial Navigation System (SINS). In this way, the IMU is expected to maintain a certain degree of accuracy by relying solely on SINS when the vehicle reaches out the range of the LBL network and cannot receive the acoustic signal. We present the acoustic LBL-based IMU online calibration model and analyze the factors that affect the accuracy of IMU calibration. The results fulfill the expectation that the gyroscope bias and accelerometer bias are the main error sources that affect the divergence of SINS position errors, and the track line of the underwater vehicle directly affects the accuracy of the calibration results. In addition, we deduce that an optimal calibration trajectory needs to consider the effects of the three-dimensional observability and position dilution of precision. In the experiment, we compare the effects of seven calibration trajectories: straight and diamond-shaped with and without the change of depth, and three sets of curves with the change of depth: circular, S-shaped, and figure-eight. Among them, we find that the figure-eight is the optimal trajectory for acoustic LBL-based IMU online calibration. We take the maintenance period during which the accumulated SINS Three Dimensional (3D) position errors are below 1 km to evaluate the calibration performance. The filed experimental results show that for the Micro-electromechanical Systems-grade IMU sensor, the maintenance period for the IMU calibrated with the proposed algorithm can be increased by 121% and 38.9% compared to the IMU without calibration and with the laboratory default parameter calibration, indicating the effectiveness of the proposed calibration algorithm.

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Autonomious Undetrwater Vehicles Navigation and Localization Systems: A Survey

Cited by 3 publications

References 59 publications

Accurate Localization in Acoustic Underwater Localization Systems

Accurate Localization in Acoustic Underwater Localization Systems

Smart Autonomous Moving Platforms

Improving the underwater navigation performance of an IMU with acoustic long baseline calibration

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