Depth sensor and DVL‐aided vertical channel damping loop design for underwater vehicles

Choe, Tok Son; Sang, Won; Park, Jin Bae

doi:10.1049/el.2015.1296

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…The depth and compass sensors are used to correct the attitude (φ, θ, ψ) errors of attitude MEMS-INS due to gyroscope drifts. The relationship between depth (z) measurements and attitude is determined by the Quaternion (q) method that is given as follows [34]:…”

Section: Depth and Compass Sensorsmentioning

confidence: 99%

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A novel GPS/ RAVO/MEMS-INS smartphone-sensor-integrated method to enhance USV navigation systems during GPS outages

Mostafa

Khater

Rizk

et al. 2019

Meas. Sci. Technol.

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Most navigation systems of unmanned surface vehicles (USVs) are based on global position system (GPS)/inertial navigation system (INS) integrated methods to improve the accuracy of the navigation system. But in some places on the Earth's surface, the GPS signal suffers from outages, interferences, and weakness, which affect the performance of the whole USV navigation system. This paper provides a continuous and accurate navigation solution via integrated GPS with micro-electro-mechanical (MEMS)-INS smartphone sensors and reduced-aided visual odometry (RAVO) using centralized Kalman filter (CKF) data fusion. The proposed RAVO is used to correct USV navigation system errors during GPS outages. It is based on integrated visual odometry (VO) with MEMS-INS smartphone sensors, the Doppler velocity log (DVL), depth and compass sensors. The DVL/depth/compass/MEMS-INS smartphone integrated solution is used to improve the performance of a VO system before data fusion processing using a CKF. The CKF is used as data fusion processing to estimate and correct USV navigation system errors.The efficiency of the USV navigation system is tested on a surface reference trajectory called Kur-Mukalla in Mukalla City, Yemen. During GPS outage for 83 s, the proposed method based on the GPS/RAVO/MEMS-INS smartphone integrated CKF method could provide a reliable USV navigation solution; it reduced the position error to 82.32% compared to the traditional GPS/MEMS-INS CKF method, and to about 70.72% compared to the GPS/Pure-VO/MEMS-INS CKF integrated method.

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Section: Depth and Compass Sensorsmentioning

confidence: 99%

“…Where U, V, and W are the velocities measured from the DVL. Equation ( 12) is simplified as follows [34]:…”

Section: Depth and Compass Sensorsmentioning

confidence: 99%

A novel GPS/ RAVO/MEMS-INS smartphone-sensor-integrated method to enhance USV navigation systems during GPS outages

Mostafa

Khater

Rizk

et al. 2019

Meas. Sci. Technol.

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“…Figure 7 shows the relationship between relative velocity τ and pitch angle θ when θ change from -10 • to 10 • . As the absolute value of the pitch angle increases, the relative velocity error increases [25]- [27]. When the underwater vehicle swings large caused by sea water fluctuates, the beam tilt angle will constantly change, making the relative speed error larger.…”

Section: B Error Model Aided By Motion Attitudementioning

confidence: 99%

A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

Wang

Yao

et al. 2019

IEEE Access

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In navigation of autonomous underwater vehicles (AUVs), the estimation of position is an important issue, especially, when the sensors such as gyroscopes contain a lot of noise, and the velocity information of Doppler velocity log (DVL) is affected by the motion attitude of the vehicle. In this paper, based on an improved auto regressive (AR) model, a real-time filter is utilized for gyroscope signal denoising. Meanwhile, according to the characteristics of the AUV, the influence of the vehicle attitude on the DVL velocity measurement error is analyzed and a motion attitude assist (MAA) method based on error model is introduced for enhancing DVL velocity accuracy. In this paper, using the proposed hybrid approach, an inertial navigation system (INS)/DVL integrated navigation system is designed. The proposed approach is evaluated by simulation and experimental test in different acceleration bound, and the existence of the DVL outage for an AUV. The results indicate that the precisions of the velocity and position are improved effectively, especially in complex motion attitude and long sailing conditions. INDEX TERMS Autonomous underwater vehicle (AUV), auto regressive (AR) model, motion attitude assist (MAA), INS/DVL integrated navigation.

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“…To cope with this dark side of SINS, non‐inertial sensors such as barometric altimeter, radar altimeter, depth sensor, a global positioning system etc., are used to provide the vertical velocity and position information [4–7]. However, in some cases such as guidance applications, the vertical information must be computed autonomously by the SINS [2].…”

Section: Introductionmentioning

confidence: 99%

Robust in‐field estimation and calibration approach for strapdown inertial navigation systems accelerometers bias acting on the vertical channel

Rahgoshay

Karimaghaie

2020

IET Radar, Sonar & Navigation

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Depth sensor and DVL‐aided vertical channel damping loop design for underwater vehicles

Cited by 6 publications

References 1 publication

A novel GPS/ RAVO/MEMS-INS smartphone-sensor-integrated method to enhance USV navigation systems during GPS outages

A novel GPS/ RAVO/MEMS-INS smartphone-sensor-integrated method to enhance USV navigation systems during GPS outages

A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

Robust in‐field estimation and calibration approach for strapdown inertial navigation systems accelerometers bias acting on the vertical channel

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