Attitude estimation and DVL based navigation using low-cost MEMS AHRS for UUVs

Ko, Nak Yong; Jeong, Seokki

doi:10.1109/urai.2014.7057502

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Moreover, even when high-end INS provide high accuracy, their high cost and complexity place constraints on the environments in which they are practical for use, leading to the development and consequent employment of MEMS (Micro Electro-Mechanical Systems) AHRS (Attitude Heading Reference System). Characterized by light weight and small sizes, they integrate a magnetometer to the INS configuration, thus being able to measure the variation of the Earth's magnetic field to estimate the best attitude of the vehicle [8,9]. Examples of AHRS are smartphones and video game consoles; it should be noted that mobile devices are able to instantly calculate pose estimation using their integrated sensors.…”

Section: Underwater Positioning Systemsmentioning

confidence: 99%

A Preliminary Study on Attitude Measurement Systems Based on Low Cost Sensors

Ciaccio

Gaglione

Troisi

2020

R3 in Geomatics: Research, Results and Review

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The increasingly use of Autonomous Underwater Vehicles (AUVs) in several context led to a rapid development and enhancement of their technologies, allowing the automatization of many tasks. One of the most challenging tasks of AUVs still remains their robust positioning and navigation, since classical global positioning techniques are generally not available for their operations. Inertial Navigation System (INS) methods provide the vehicle current position and orientation integrating data acquired by the internal accelerometer and gyroscope. This system has the advantage of not needing to either send or receive signals from other systems; however, among the errors the sensors are mainly affected by, the most critical one is related to their drift, which makes the position error growing over time. The attenuation of the effect of these problematics is generally achieved combining different positioning methods, as for example acoustic-or geophysical-based ones. An accurate estimation of the device orientation is anyway necessary to get satisfying results in terms of position and autonomous navigation. In this paper, a preliminary study on the use of smartphone low-cost sensors to perform attitude estimation is presented. With the final aim of developing a cheaper and more accessible underwater positioning system, a first analysis is conducted to verify the accuracy of the attitude angles obtained by the integration of smartphone data acquired in different operative settings. Different filtering methods will be employed.

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Section: Underwater Positioning Systemsmentioning

confidence: 99%

A Preliminary Study on Attitude Measurement Systems Based on Low Cost Sensors

Ciaccio

Gaglione

Troisi

2020

R3 in Geomatics: Research, Results and Review

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show abstract

“…With the rapid development of the low-cost Micro Electro-Mechanical Systems (MEMS) inertial sensors, Attitude and Heading Reference Systems (AHRS) made up of several MEMS inertial sensors have become popular choices for a part of small-scale shallow sea AUVs, which can be integrated with DVL to perform AUV navigation [11]- [13]. During a specific mission, the data collected by different sensors need to be processed together to acquire an optimal estimate of the AUV position.…”

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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“…In general, the underwater navigation of the UUV is based on the inertial navigation system (INS), which may cause the measurement error to accumulate and enlarge as time goes by [2]. In addition, the dead reckoning system (DR) composed of Doppler Velocity Log (DVL) and compass is also a common system of underwater navigation [3], which calculates the position information according to the velocity information of DVL and the azimuth information of compass. In order to improve the navigation accuracy, the inertial navigation system is often combined with the DR system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive H-Infinity Cubature Kalman Filter Algorithm Based on Sage-Husa Estimator for Unmanned Underwater Vehicle

Yang

Zhang

et al. 2020

Mathematical Problems in Engineering

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In the navigation of unmanned underwater vehicle (UUV), a filtering algorithm suitable for the working conditions is required. Due to the disturbance from the environment and maneuverability, outliers and noise with time-varying statistical properties always exist, which greatly affect the positioning accuracy and stability of the navigation system. In this paper, we present a novel nonlinear state estimation algorithm named AH∞CKF based on the combination of H∞CKF and Sage-Husa estimator. The recently developed H∞CKF provides nonlinear filtering good robustness, and Sage-Husa estimator could timely modify the statistical properties of noise. The novel algorithm is superior to H∞CKF in accuracy by combining Sage-Husa estimator with the H∞CKF while ensuring robustness. The effectiveness of the novel AH∞CKF is verified by lake experiment and simulation.

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Attitude estimation and DVL based navigation using low-cost MEMS AHRS for UUVs

Cited by 10 publications

References 5 publications

A Preliminary Study on Attitude Measurement Systems Based on Low Cost Sensors

A Preliminary Study on Attitude Measurement Systems Based on Low Cost Sensors

Navnet: AUV Navigation Through Deep Sequential Learning

A Novel Adaptive H-Infinity Cubature Kalman Filter Algorithm Based on Sage-Husa Estimator for Unmanned Underwater Vehicle

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