A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering

Yan, Xiao; Yang, Yipeng; Luo, Qinghua; Chen, Yunsai; Hu, Cong

doi:10.3390/s19204576

Cited by 10 publications

(4 citation statements)

References 22 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Mehrjouyan and Alfi (2019), a robust adaptive unscented Kalman filter (RAUKF) method is presented, and the main focus is given to the 3D bearings-only target tracking (BOT) problem; however, simulation results only are presented. In Yan et al (2019), a strapdown INS (SINS)/DVL navigation solution based on a filtering gain compensation adaptive filtering method is shown, but simulations only are proposed. In Davari and Gholami (2019), an adaptive Kalman filtering algorithm for asynchronous multirate systems is presented.…”

Section: Related Workmentioning

confidence: 99%

Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter‐based strategy for AUVs

Franchi

Ridolfi

Allotta

2020

Journal of Field Robotics

View full text Add to dashboard Cite

One of the most significant challenges in the underwater domain is to retrieve the autonomous underwater vehicle (AUV) position within the surrounding environment. Indeed, reliable navigation systems are fundamental to perform complex tasks and missions. Most of the navigation filters for AUVs are based on Bayesian estimators such as the linear Kalman Filter (KF), the extended KF, the unscented KF, or the particle filter where, usually, different instruments including a Doppler velocity log (DVL) contribute to the localization task. The usage of forward-looking SONARs (FLS) in navigation-aiding is, most of the time, devoted to limiting the navigation drift of the AUV by using simultaneous localization and mapping methods. Therefore, these devices are commonly employed with a standard navigation sensors set comprising an attitude heading reference system and a DVL. In this contribution, the authors propose a novel navigation strategy specifically tailored to AUVs based on an adaptive unscented KF, where linear speed estimations are obtained with a 2D FLS instead of with a DVL and therefore promoting the employment of FLSs as an aid for underwater navigation. The marine robotics community could gain significant benefits from reliable navigation achieved with an FLS-based navigation architecture. Most importantly, a single FLS can be used for imaging-related applications (i.e., sonograms acquisition) and navigation, where, instead, different dedicated devices are currently employed for the two tasks. Smaller AUVs usually possess reduced payload carrying capabilities; thus, multitasking use of onboard sensors, which leads to compactness, is a desirable feature. Navigation data obtained during sea trials performed in La Spezia (Italy) at the NATO STO Centre for Maritime Research and Experimentation has been used for offline validation. Afterward, the online results of real autonomous underwater missions undertaken in La Spezia (Italy) and at Vulcano Island, Messina (Italy), are reported.

show abstract

Section: Related Workmentioning

confidence: 99%

Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter‐based strategy for AUVs

Franchi

Ridolfi

Allotta

2020

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…Klein et al (2011) use a virtual GPS satellite when a partial signal receives from GPS. In Yan et al (2019), a strap-down inertial navigation system (SINS)/DVL-integrated positioning system based on an adaptive filter with filtering gain compensation is offered to utilize when the navigation system detects a sudden change in the state of the vehicle motion. Gao et al (2015) consider estimating the process state and statistical characteristics of the noise of the INS/DVL navigation system when the statistics of the process and measurement noise are unknown or time-varying.…”

Section: Introductionmentioning

confidence: 99%

Integrated navigation system (INS/auxiliary sensor) based on adaptive robust Kalman filter with partial measurements

Hosseini

Noei

Sadati

2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Inertial navigation system (INS) is the most common positioning system in the underwater vehicle due to the limitations of penetration of the global navigation system. Amendment of INS cumulative error is made possible using data of auxiliary sensors such as Doppler velocity log (DVL). Due to the environmental conditions of the seabed, access to full DVL beams is impractical. Moreover, the statistical characteristics of measuring noise are unknown. These dramatically reduce the performance of the navigation system. Therefore, a loosely coupled (LC) approach is used to fuse partial DVL measurements in this study. This approach benefits from a virtual beam containing the velocity obtained in the last INS step instead of inaccessible DVL beams. To deal with the unknown statistical characteristics of the measurement and the limitations of access to DVL beams, an adaptive robust Kalman filter (ARKF) based on the Huber cost function is proposed. The performance of the proposed integrated navigation system for a remotely operated vehicle is investigated in the presence of observation noise and outliers. Results show that the proposed ARKF is robust against vigorous maneuvers, improves the estimation accuracy effectively, and can effectively reject measurement outliers.

show abstract

“…Integrated navigation systems have become one of the research hotspots in the field of navigation, which are mainly SINS and supplemented by GNSS, DVL, odometer (OD), and other systems [ 1 , 2 , 3 , 4 ]. A reasonable model is one of the keys for the integrated navigation system to accomplish navigation task.…”

Section: Introductionmentioning

confidence: 99%

Research on the Necessity of Lie Group Strapdown Inertial Integrated Navigation Error Model Based on Euler Angle

Qian

Qin

et al. 2022

Sensors

View full text Add to dashboard Cite

In response to the lack of specific demonstration and analysis of the research on the necessity of the Lie group strapdown inertial integrated navigation error model based on the Euler angle, two common integrated navigation systems, strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) and strapdown inertial navigation system/doppler velocity log (SINS/DVL), are used as subjects, and the piecewise constant system (PWCS) matrix, based on the Lie group error model, is established. From three aspects of variance estimation, the observability and performance of the system with large misalignment angles for low, medium, and high accuracy levels, traditional error model, Lie group left error model, and right error model are compared. The necessity of research on Lie group error model is analyzed quantitatively and qualitatively. The experimental results show that Lie group error model has better stability of variance estimation, estimation accuracy, and observability than traditional error model, as well as higher practical value.

show abstract

A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering

Cited by 10 publications

References 22 publications

Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter‐based strategy for AUVs

Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter‐based strategy for AUVs

Integrated navigation system (INS/auxiliary sensor) based on adaptive robust Kalman filter with partial measurements

Research on the Necessity of Lie Group Strapdown Inertial Integrated Navigation Error Model Based on Euler Angle

Contact Info

Product

Resources

About