Inertial Navigation System/Doppler Velocity Log (INS/DVL) Fusion with Partial DVL Measurements

Tal, Asaf; Klein, Itzik; Katz, Reuven

doi:10.3390/s17020415

Cited by 109 publications

(42 citation statements)

References 13 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Case 1: The amplitudes of wind and wave are small, the initial azimuth is 30°, and parameters of attitude angles and speed are listed in Table 1 (Tal, Klein, & Katz, 2017). Case 2: The amplitudes of wind and wave are big, the initial azimuth is 120°, and parameters of attitude angles and speed are listed in Table 2 (Kang et al., 2014).…”

Section: Simulations and Experimentsmentioning

confidence: 99%

A novel in‐motion alignment method for underwater SINS using a state‐dependent Lie group filter

Pei

Ning

et al. 2020

Navigation

View full text Add to dashboard Cite

The in‐motion alignment for the underwater strapdown inertial navigation system is still a challenging problem due to various disturbances in the underwater environment. In this paper, a novel in‐motion alignment method, based on the Lie group representation, is developed. In this method, the process model is rewritten using the Lie group of the constant attitude matrix between two inertial frames as the state. An exact linear measurement model is constructed by analyzing the effect of the sensor errors in calculating the velocity vector. Next, the state‐dependent Lie group filter is designed basing on accurate derivation expressions for the covariance matrices of state‐dependent noises. The simulation and experiment results demonstrate that the proposed method can achieve better alignment accuracy and time than the existing method. The accuracy improves by 70% with the quaternion Kalman filter.

show abstract

Section: Simulations and Experimentsmentioning

confidence: 99%

A novel in‐motion alignment method for underwater SINS using a state‐dependent Lie group filter

Pei

Ning

et al. 2020

Navigation

View full text Add to dashboard Cite

show abstract

“…Different tightly coupled navigation filters have been designed over the past few years [14,15]. A tightly coupled system uses the velocity measured by each transducer, not the velocity from the body frame transformed from filter measurements.…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement for Conventional Tightly Coupled INS/DVL Navigation System Using Regeneration of Partial DVL Measurements

Yoo¹,

Kim²,

Yoon

et al. 2020

Journal of Sensors

View full text Add to dashboard Cite

Inertial navigation systems/Doppler velocity log (INS/DVL) integrated navigation systems are widely used in underwater environments where GPS is unavailable. An INS/DVL integrated navigation system is generally loosely coupled; however, this does not work if any of the DVL transducers do not work. If a system is tightly coupled, velocity error can be estimated with fair accuracy even if some of the transducers fail. However, despite the robustness of a tightly coupled system compared to a loosely coupled one, velocity error estimation accuracy of the former decreases as the number of faulty transducers increases. Therefore, this paper proposes an INS/DVL/revolutions per minute (RPM) integrated navigation filter designed to improve the performance of conventional tightly coupled integrated systems by estimating data from faulty transducers using RPM data. Two salient features of the proposed filter are (1) estimating RPM data accounting for error from the effect of tidal currents and (2) continuous estimation of error in RPM data by selectively converting only the measurements of faulty transducers. The performance of the proposed filter was first verified using Monte Carlo numerical simulations with the analysis range set to 1 standard deviation (1σ, 68%) and then with real sea test measurement data.

show abstract

“…However, this method requires the AUV to surface for position correction. In order to alleviate the problem of DVL when it fails to maintain a bottom lock, Tal et al [35] proposed a navigation system based on INS, aided by a DVL, magnetometer, and pressure sensor (PS).…”

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Simultaneous Localization and Mapping Online Algorithm Based on Neural Network

Hou

Shao

Zou

et al. 2019

IJGI

View full text Add to dashboard Cite

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

show abstract

Inertial Navigation System/Doppler Velocity Log (INS/DVL) Fusion with Partial DVL Measurements

Cited by 109 publications

References 13 publications

A novel in‐motion alignment method for underwater SINS using a state‐dependent Lie group filter

A novel in‐motion alignment method for underwater SINS using a state‐dependent Lie group filter

Performance Enhancement for Conventional Tightly Coupled INS/DVL Navigation System Using Regeneration of Partial DVL Measurements

A Novel Underwater Simultaneous Localization and Mapping Online Algorithm Based on Neural Network

Contact Info

Product

Resources

About