An Underwater Integrated Navigation Algorithm to Deal With DVL Malfunctions Based on Deep Learning

Ding, Li; Xu, Jiangning; He, Hongyang; Wu, Miao

doi:10.1109/access.2021.3083493

Cited by 33 publications

(14 citation statements)

References 26 publications

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“…The main advantage of this methodology is that it does not require a training stage, the grid cell behavior emerges from the population of neurons based on the defined structure of the network. Another benefit of this method is the low number of neurons utilized when compared with other neural networks based-approaches, such as deep neural networks [31]. Furthermore, these types of neurons have the capability of learning and to update in a more efficient way, both energy and data wise [32], but would require specific hardware [33], such as the Neurogrid [34] or TrueNorth [35] to do so.…”

Section: Discussionmentioning

confidence: 99%

Marine Vehicles Localization Using Grid Cells for Path Integration

Carlucho¹,

F.²,

Paula³

et al. 2021

Preprint

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Autonomous Underwater Vehicles (AUVs) are platforms used for research and exploration of marine environments. However, these types of vehicles face many challenges that hinder their widespread use in the industry. One of the main limitations is obtaining accurate position estimation, due to the lack of GPS signal underwater. This estimation is usually done with Kalman filters. However, new developments in the neuroscience field have shed light on the mechanisms by which mammals are able to obtain a reliable estimation of their current position based on external and internal motion cues. A new type of neuron, called Grid cells, has been shown to be part of path integration system in the brain. In this article, we show how grid cells can be used for obtaining a position estimation of underwater vehicles. The model of grid cells used requires only the linear velocities together with heading orientation and provides a reliable estimation of the vehicle's position. We provide simulation results for an AUV which show the feasibility of our proposed methodology.

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Section: Discussionmentioning

confidence: 99%

Marine Vehicles Localization Using Grid Cells for Path Integration

Carlucho¹,

F.²,

Paula³

et al. 2021

Preprint

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show abstract

“…Then the principle of obtaining

is to obtain

in turn according to the principle that a single variable is gradually introduced. For the specific calculation process can refer to [ 34 ], and the specific algorithm flow is given in the next section. At the same time, after fixed-point iterations of times N , the variational approximation of the posterior probability density function is given by the Equation (36).…”

Section: Principle Of Support Vector Regression Assisted Adaptive Filtermentioning

confidence: 99%

“…Ref. [ 34 ] proposes a robust filtering algorithm based on Mahalanobis distance (HRAKF). The above three methods can eliminate outliers, but the adaptive ability is reduced.…”

Section: Introductionmentioning

confidence: 99%

A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System

Zhu

Qin

et al. 2022

Sensors

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As an important means of underwater navigation and positioning, the accuracy of SINS/DVL integrated navigation system greatly affects the efficiency of underwater work. Considering the complexity and change of the underwater environment, it is necessary to enhance the robustness and adaptability of the SINS/DVL integrated navigation system. Therefore, this paper proposes a new adaptive filter based on support vector regression. The method abandons the elimination of outliers generated by Doppler Velocity Logger (DVL) in the measurement process from the inside of the filter in the form of probability density function modeling. Instead, outliers are eliminated from the perspective of external sensors, which effectively improves the robustness of the filter. At the same time, a new Variational Bayesian (VB) strategy is adopted to reduce the influence of inaccurate process noise and measurement noise, and improve the adaptiveness of the filter. Their advantages complement each other, effectively improve the stability of filter. Simulation and ship-borne tests are carried out. The test results show that the method proposed in this paper has higher navigation accuracy.

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“…Traditional methods for underwater navigation are based on using an inertial navigation system (INS) [14], [15] and Doppler velocity log (DVL) [16]. However, an INS is highly affected by drift errors and need the vehicle to return to the surface for resetting its position [12], [15].…”

Section: Introductionmentioning

confidence: 99%

“…However, an INS is highly affected by drift errors and need the vehicle to return to the surface for resetting its position [12], [15]. The DVL is highly sensitive to the external environment conditions [16],…”

Section: Introductionmentioning

confidence: 99%

Sonar FoV Segmentation for Motion Estimation Using DL Networks

2022

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Accurate navigation of an autonomous underwater vehicle is important for its reliable operation. However, this task is challenging due to limitations of radio wave propagation and poor visibility in the aquatic environment. Underwater navigation techniques based on analysing sonar images facilitated by machine learning have shown promising results. However, previously proposed techniques are still complicated for real-time applications. This paper investigates low complexity techniques for the motion estimation based on the use of images obtained by a sonar looking down to the seafloor. The sonar can use multiple beams within a field of view (FoV). Various configurations of beams are considered according to portions of the FoV covered and two estimation approaches are investigated. In one approach, the sonar images are directly processed by a deep learning (DL) network, whereas in the other, the images are converted into (reduced size) vectors before applying them to a DL network. The vector approach shows a significantly lower computation time (about 10 times faster), which makes it suitable for realtime applications. Both the approaches show a similar estimation accuracy, about 10% of the maximum magnitude of the motion. The vector technique has been used to estimate a simulated trajectory and compare the estimate with the ground truth, which showed a good match. It has also been applied to estimate the trajectory of an imaging sonar from a real data set from a ship's hull inspection. The estimated trajectory has successfully been used to build a mosaic by merging the sonar images from the real data set.

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An Underwater Integrated Navigation Algorithm to Deal With DVL Malfunctions Based on Deep Learning

Cited by 33 publications

References 26 publications

Marine Vehicles Localization Using Grid Cells for Path Integration

Marine Vehicles Localization Using Grid Cells for Path Integration

A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System

Sonar FoV Segmentation for Motion Estimation Using DL Networks

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