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22 pages, 8270 KB

Open AccessArticle

Event-Triggered State Filter Estimation for INS/DVL Integrated Navigation with Correlated Noise and Outliers

by Xiaolei Ma, Zhengrong Wei, Weicheng Liu and Shengli Wang

Sensors 2025, 25(5), 1545; https://doi.org/10.3390/s25051545 - 2 Mar 2025

Cited by 2 | Viewed by 1369

The Inertial Navigation System (INS) and Doppler Velocity Log (DVL) combination navigation system has been widely used in Autonomous Underwater Vehicles (AUVs) due to its independence, stealth, and high accuracy. Compared to the standalone INS or DVL, the integrated system provides continuous and accurate navigation information. However, the underwater environment is complex, and system noise and observation noise may not satisfy the condition of mutual independence. In addition, the DVL may produce abnormal measurement values during operation. In this study, an Event-Triggered Correlation Noise Filter (ETCNF) method was designed for fusing INS and DVL data. An auxiliary matrix was introduced to decouple the correlated noise, allowing novel state estimation. Moreover, the event-triggered mechanism detected and eliminated abnormal values in DVL measurements, which improved the positioning accuracy and robustness of the INS/DVL integrated system. Finally, simulation experiments were conducted to verify the effectiveness and superiority of the proposed algorithm. Full article

(This article belongs to the Section Remote Sensors)

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19 pages, 6918 KB

Open AccessArticle

Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and INS/DVL/Gyrocompass Using Micro-Electro-Mechanical System Sensors

by Gen Fukuda and Nobuaki Kubo

Sensors 2022, 22(14), 5334; https://doi.org/10.3390/s22145334 - 17 Jul 2022

Cited by 10 | Viewed by 3577

Abstract

This article proposes a novel initial bias estimation method using a trajectory generator (TG). The accuracy of attitude and position estimation in navigation after using the inertial navigation system/Doppler velocity log (INS/DVL) and INS/DVL/gyrocompass (IDG) for 1 h were evaluated, and the results were compared to those obtained using the conventional Kalman filter (KF) estimation method. The probability of a horizontal position error < 1852 m (1 nautical mile) with a bias interval > 400 s was 100% and 9% for the TG and KF, respectively. In addition, the IDG average horizontal position errors over 1 h were 493 m and 507 m for the TG and KF, respectively. Moreover, the amount of variation was 2 m and 27 m for the TG and the KF, respectively. Thus, the proposed method is effective for initial bias estimation of INS/DVL and IDG using micro-electro-mechanical system sensors on a constantly moving vessel. Full article

(This article belongs to the Special Issue Sensors for Navigation and Control Systems)

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14 pages, 867 KB

Open AccessArticle

Adaptive Maximum Correntropy Gaussian Filter Based on Variational Bayes

by Guoqing Wang, Zhongxing Gao, Yonggang Zhang and Bin Ma

Sensors 2018, 18(6), 1960; https://doi.org/10.3390/s18061960 - 17 Jun 2018

Cited by 30 | Viewed by 4628

Abstract

In this paper, we investigate the state estimation of systems with unknown covariance non-Gaussian measurement noise. A novel improved Gaussian filter (GF) is proposed, where the maximum correntropy criterion (MCC) is used to suppress the pollution of non-Gaussian measurement noise and its covariance is online estimated through the variational Bayes (VB) approximation. MCC and VB are integrated through the fixed-point iteration to modify the estimated measurement noise covariance. As a general framework, the proposed algorithm is applicable to both linear and nonlinear systems with different rules being used to calculate the Gaussian integrals. Experimental results show that the proposed algorithm has better estimation accuracy than related robust and adaptive algorithms through a target tracking simulation example and the field test of an INS/DVL integrated navigation system. Full article

(This article belongs to the Section Physical Sensors)

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17 pages, 4188 KB

Open AccessArticle

An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System

by Yiqing Yao, Xiaosu Xu and Xiang Xu

Sensors 2017, 17(9), 2030; https://doi.org/10.3390/s17092030 - 5 Sep 2017

Cited by 37 | Viewed by 6637

Abstract

Inertial navigation system (INS)/Doppler velocity log (DVL) integration is the most common navigation solution for underwater vehicles. Due to the complex underwater environment, the velocity information provided by DVL always contains some errors. To improve navigation accuracy, zero velocity update (ZUPT) technology is considered, which is an effective algorithm for land vehicles to mitigate the navigation error during the pure INS mode. However, in contrast to ground vehicles, the ZUPT solution cannot be used directly for underwater vehicles because of the existence of the water current. In order to leverage the strengths of the ZUPT method and the INS/DVL solution, an interactive multiple model (IMM)-aided ZUPT methodology for the INS/DVL-integrated underwater navigation system is proposed. Both the INS/DVL and INS/ZUPT models are constructed and operated in parallel, with weights calculated according to their innovations and innovation covariance matrices. Simulations are conducted to evaluate the proposed algorithm. The results indicate that the IMM-aided ZUPT solution outperforms both the INS/DVL solution and the INS/ZUPT solution in the underwater environment, which can properly distinguish between the ZUPT and non-ZUPT conditions. In addition, during DVL outage, the effectiveness of the proposed algorithm is also verified. Full article

(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)

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20 pages, 5264 KB

Open AccessTechnical Note

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

by Asaf Tal, Itzik Klein and Reuven Katz

Sensors 2017, 17(2), 415; https://doi.org/10.3390/s17020415 - 22 Feb 2017

Cited by 147 | Viewed by 15909

Abstract

The Technion autonomous underwater vehicle (TAUV) is an ongoing project aiming to develop and produce a small AUV to carry on research missions, including payload dropping, and to demonstrate acoustic communication. Its navigation system is based on an inertial navigation system (INS) aided by a Doppler velocity log (DVL), magnetometer, and pressure sensor (PS). In many INSs, such as the one used in TAUV, only the velocity vector (provided by the DVL) can be used for aiding the INS, i.e., enabling only a loosely coupled integration approach. In cases of partial DVL measurements, such as failure to maintain bottom lock, the DVL cannot estimate the vehicle velocity. Thus, in partial DVL situations no velocity data can be integrated into the TAUV INS, and as a result its navigation solution will drift in time. To circumvent that problem, we propose a DVL-based vehicle velocity solution using the measured partial raw data of the DVL and additional information, thereby deriving an extended loosely coupled (ELC) approach. The implementation of the ELC approach requires only software modification. In addition, we present the TAUV six degrees of freedom (6DOF) simulation that includes all functional subsystems. Using this simulation, the proposed approach is evaluated and the benefit of using it is shown. Full article

(This article belongs to the Special Issue Inertial Sensors and Systems 2016)

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18 pages, 786 KB

Open AccessArticle

A Novel INS and Doppler Sensors Calibration Method for Long Range Underwater Vehicle Navigation

by Kanghua Tang, Jinling Wang, Wanli Li and Wenqi Wu

Sensors 2013, 13(11), 14583-14600; https://doi.org/10.3390/s131114583 - 28 Oct 2013

Cited by 65 | Viewed by 6697

Abstract

Since the drifts of Inertial Navigation System (INS) solutions are inevitable and also grow over time, a Doppler Velocity Log (DVL) is used to aid the INS to restrain its error growth. Therefore, INS/DVL integration is a common approach for Autonomous Underwater Vehicle (AUV) navigation. The parameters including the scale factor of DVL and misalignments between INS and DVL are key factors which limit the accuracy of the INS/DVL integration. In this paper, a novel parameter calibration method is proposed. An iterative implementation of the method is designed to reduce the error caused by INS initial alignment. Furthermore, a simplified INS/DVL integration scheme is employed. The proposed method is evaluated with both river trial and sea trial data sets. Using 0.03°/h(1σ) ring laser gyroscopes, 5 × 10⁻⁵ g(1σ) quartz accelerometers and DVL with accuracy 0.5% V ± 0.5 cm/s, INS/DVL integrated navigation can reach an accuracy of about 1‰ of distance travelled (CEP) in a river trial and 2‰ of distance travelled (CEP) in a sea trial. Full article

(This article belongs to the Section Physical Sensors)

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