A tracking of AUV with integration of SSBL acoustic positioning and transmitted INS data

Watanabe, Yoshitaka; Ochi, Hiroshi; Shimura, Takuya; Hattori, Tetsutaro

doi:10.1109/oceanse.2009.5278145

Cited by 10 publications

(4 citation statements)

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“…Literature (Li et al, 2016) proposed a multi-model EKF integrated navigation algorithm to filter outliers, which exploits calculating the innovation between predicted measurement and actual measurement by DR and LBL. An accurate tracking method using EKF to estimate AUV position by super short baseline (SSBL) from the mother ship is proposed in (Watanabe et al, 2009), which well compensates the random error of the SSBL and the inertial navigation system (INS) drift. Localization of an AUV Using Graph-based simultaneous localization and mapping (SLAM) algorithm were tested underwater (Paull et al, 2014;Lee et al, 2013;Bonin Negreet al, 2015), in which a graph structure was built using the landmark detection results by vision camera and DR data of the AUV.…”

Section: Review Of Previous Workmentioning

confidence: 99%

A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles

et al. 2019

Ocean Engineering

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Section: Review Of Previous Workmentioning

confidence: 99%

A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles

et al. 2019

Ocean Engineering

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“…On its essence, they all rely on computing the position of the vehicle from a set of acoustic ranges or bearings between a vehicle and a set of beacons, deployed at predefined fixed locations, whether at the surface or at the bottom of the sea. Examples of AUV tracking are found for instance by Watanabe et al (2009), on which an accurate tracking method to estimate the position of AUV relative to a mother ship is described, using a Super-Short Baseline Navigation acoustic network. On the other hand, Melo and Matos (2008) demonstrate how tracking an AUV can be performed with an Inverted Long Baseline Navigation method with only two acoustic beacons.…”

Section: Related Workmentioning

confidence: 99%

Tracking multiple Autonomous Underwater Vehicles

Melo

Matos

2018

Auton Robot

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In this paper we present a novel method for the acoustic tracking of multiple Autonomous Underwater Vehicles. While the problem of tracking a single moving vehicle has been addressed in the literature, tracking multiple vehicles is a problem that has been overlooked, mostly due to the inherent difficulties on data association with traditional acoustic localization networks. The proposed approach is based on a Probability Hypothesis Density Filter, thus overcoming the data association problem. Our tracker is able not only to successfully estimate the positions of the vehicles, but also their velocities. Moreover, the tracker estimates are labelled, thus providing a way to establish track continuity of the targets. Using real word data, our method is experimentally validated and the performance of the tracker is evaluated.

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“…By measuring the Time of Flight (TOF) of signals from acoustic beacons or modems, acoustic navigation techniques have been applied to perform AUV navigation. However, acoustic beacons need to be installed in advance, which limits the operating area to a specific scope [7]. Moreover, the cost of the installation and maintenance for acoustic beacons can be hardly affordable for developing small-scale shallow sea AUVs.…”

Section: Introductionmentioning

confidence: 99%

Navnet: AUV Navigation Through Deep Sequential Learning

Zhang

et al. 2020

IEEE Access

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Achieving accurate navigation and localization is crucial for Autonomous Underwater Vehicle (AUV). Traditional navigation algorithms, such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), require the system model and measurement model for state estimation to obtain the AUV position. However, this may introduce modeling errors and state estimation errors which will affect the final precision of AUV navigation system to a certain extent. To avoid these problems, in this paper, we proposed a deep framework-NavNet-by taking AUV navigation as a deep sequential learning problem. Firstly, the proposed NavNet can take raw sensor data at different frequencies as input, which benefits from the sequential learning capability of Recurrent Neural Network (RNN). Secondly, NavNet takes advantage of a simplified attention mechanism and Fully Connected (FC) layers to output AUV displacements per unit time, which accomplishes low-frequency AUV navigation by accumulation of it. More importantly, there is no need for the model building and state estimation with NavNet, which avoids the import of relevant errors. We compare the performance of NavNet to EKF and UKF using collected data by running Sailfish in the sea. Experimental results show that NavNet has an excellent performance in terms of both the navigation accuracy and fault tolerance. In addition, a reliable fusion strategy of NavNet and conventional method is applied to achieve high-frequency AUV navigation. The experimental results show that the proposed architecture can be a reliable supplement to limit the error growth of conventional algorithms. INDEX TERMS Autonomous underwater vehicle, navigation, extended Kalman filter, unscented Kalman filter, sequential learning.

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A tracking of AUV with integration of SSBL acoustic positioning and transmitted INS data

Cited by 10 publications

References 9 publications

A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles

A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles

Tracking multiple Autonomous Underwater Vehicles

Navnet: AUV Navigation Through Deep Sequential Learning

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