DVL Velocity Aiding in the HUGIN 1000 Integrated Inertial Navigation System

Jalving, B.; Gade, Kenneth; Svartveit, Kristian; Willumsen, Are Baardsgaard; Sørhagen, Robert

doi:10.4173/mic.2004.4.2

Cited by 96 publications

(71 citation statements)

References 2 publications

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“…According to Jalving et al (2004), the main contributors to position drift in a DVL aided INS is error in the body-fixed velocity, and error in heading. The velocity error are mainly determined by the accuracy of the DVL itself (0.2% of distance traveled (DT) for a 1200 kHz DVL according to Jalving et al (2004)).…”

Section: Inertial Navigationmentioning

confidence: 99%

“…• latitude (see Jalving et al (2004)). Therefore, in long missions, or missions without accurate velocity measurements, some sort of position measurements should be made, to correct the unbounded error that is an inherent problem of pure inertial navigation.…”

Section: Inertial Navigationmentioning

confidence: 99%

“…The velocity error are mainly determined by the accuracy of the DVL itself (0.2% of distance traveled (DT) for a 1200 kHz DVL according to Jalving et al (2004)). Heading error will be a function of gyro drift (in the range 0.005-1…”

Section: Inertial Navigationmentioning

confidence: 99%

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Using Autonomous Underwater Vehicles as Sensor Platforms for Ice-Monitoring

Norgren¹,

Skjetne

2014

MIC

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Due to the receding sea-ice extent in the Arctic, and the potentially large undiscovered petroleum resources present north of the Arctic circle, offshore activities in ice-infested waters are increasing. Due to the presence of drifting sea-ice and icebergs, ice management (IM) becomes an important part of the offshore operation, and an important part of an IM system is the ability to reliably monitor the ice conditions. An autonomous underwater vehicle (AUV) has a unique capability of high underwater spatial and temporal coverage, making it suitable for monitoring applications. Since the first Arctic AUV deployment in 1972, AUV technology has matured and has been used in complex under-ice operations. This paper motivates the use of AUVs as an ice-monitoring sensor platform. It discusses relevant sensor capabilities and challenges related to communication and navigation. This paper also presents experiences from a field campaign that took place in Ny-Ålesund at Svalbard in January 2014, where a REMUS 100 AUV was used for sea-floor mapping and collection of oceanographic parameters. Based on this, we discuss the experiences related to using AUVs for ice-monitoring. We conclude that AUVs are highly applicable for ice-monitoring, but further research is needed.

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Section: Inertial Navigationmentioning

confidence: 99%

Section: Inertial Navigationmentioning

confidence: 99%

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Using Autonomous Underwater Vehicles as Sensor Platforms for Ice-Monitoring

Norgren¹,

Skjetne

2014

MIC

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“…For the feature of self-contained and continuity of navigation information, the SINS becomes a key navigation equipment for the underwater vehicles' navigation [3,4,5,6]. However, owing to the drift of the IMU measurements, a single SINS cannot afford precise position and velocity for a long period of time without any aiding methods.…”

Section: Introductionmentioning

confidence: 99%

A fast in-situ SINS and Doppler sensor calibration algorithm for underwater vehicle navigation

Song

2014

IEICE Electron. Express

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This paper reports the development and experimental evaluation of an in-situ calibration algorithm of the misalignment mounting angle matrix between the Strapdown inertial navigation system (SINS) and Doppler sensor which are practically used for accurate underwater navigation. Most previously reported methods required the SINS to be aligned first to output accurate attitude before the calibration. By separately treating the body frame and the navigation frame attitude update, the algorithm in this paper could be simultaneously carried out during the SINS attitude alignment stage. Simulation and experiment results show that the calibration could be done in 300 s and the position error during the navigation is less than 0.8% of the voyage distance.

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“…Now the integrated navigation system which consists of multiple navigation sensors is widely adopted on underwater vehicles [6][7][8]. As a high-precision velocity measuring instrument, the Doppler velocity log (DVL) has become increasingly popular as the fundamental component of underwater integrated navigation systems [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Zhu

Cheng

et al. 2017

Applied Sciences

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Abstract:As a common device for underwater integrated navigation systems, Doppler velocity log (DVL) has the risk of malfunction. To improve the reliability of navigation systems, a hybrid approach is presented to forecast the measurements of the DVL while it malfunctions. The approach employs partial least squares regression (PLSR) coupled with support vector regression (SVR) to build a hybrid predictor. As the current and past calculating velocities of strapdown inertial navigation system (SINS) are taken as the predictor's inputs, PLSR is applied to cope with the situation where there exists intense relativity among independent variables. Since PLSR is a linear regression, SVR is used to predict the residual components of the PLSR prediction to improve the accuracy. When the DVL works well, the hybrid predictor is trained online as a backup, whereas during malfunctions, the predictor offers the estimation of the DVL measurements for information fusion. The performance of the proposed approach is verified with simulations based on SINS/DVL/MCP/pressure sensor (PS) integrated navigation system. The comparison results indicate that the PLSR-SVR hybrid predictor can correctly provide the estimated DVL measurements and effectively extend the tolerance time on DVL malfunctions, thereby improving the navigation accuracy and reliability.

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DVL Velocity Aiding in the HUGIN 1000 Integrated Inertial Navigation System

Cited by 96 publications

References 2 publications

Using Autonomous Underwater Vehicles as Sensor Platforms for Ice-Monitoring

Using Autonomous Underwater Vehicles as Sensor Platforms for Ice-Monitoring

A fast in-situ SINS and Doppler sensor calibration algorithm for underwater vehicle navigation

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

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