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

Yao, Yiqing; Xu, Xiaosu; Xu, Xiang

doi:10.3390/s17092030

Cited by 29 publications

(16 citation statements)

References 21 publications

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“…In this paper, the main target is to deal with the Self-Aided SINS scheme so that the errors of IMU are assumed to be random walk bias error and white Gaussian noise. Thus, the error propagation models of gyroscopes and accelerometers can be constructed as follows [9], [21], [22]:…”

Section: A Mechanism and Error Model Of Sinsmentioning

confidence: 99%

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SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

et al. 2021

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Motivated by the problem that water-track Doppler Velocity Log (DVL) cannot effectively suppress the error accumulation of strap-down inertial navigation system (SINS), this paper proposes a novel SINS/DVL integrated navigation algorithm for deep and long cruising range Human Occupied Vehicle (HOV). Such algorithm decomposes the navigation process into two tightly coupled working modes: alignment mode and navigation mode. In the alignment mode, HOV is controlled to perform horizontal circular motion for several minutes to realize Self-Aided SINS. Combining the precise navigation solutions provided by Self-Aided SINS and measurements from DVL with water track, recursive least square (RLS) algorithm is adopted to estimate heading misalignment angle between SINS and DVL and horizontal ocean current velocity. In the navigation mode, both horizontal ocean current velocity obtained in the alignment mode and DVL measurements are utilized to assist SINS, thus enabling SINS/DVL/Current integrated navigation. A square trajectory with a navigation-grade inertial measurement unit (IMU) is simulated to evaluate the proposed SINS/DVL integrated navigation algorithm. Simulation results show that the proposed SINS/DVL integrated navigation is capable of suppressing SINS error divergence effectively and efficiently. In addition, the feasibility and effectiveness of Self-Aided SINS based on horizontal circular motion is also verified by field test with real IMU data.

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Section: A Mechanism and Error Model Of Sinsmentioning

confidence: 99%

“…Here, the gyro random biases were all set as 0.01 / h and random noises were white Gaussian noise with zero mean and standard deviation of 0.01 / h [21]. And the accelerometer random biases were all set as 50 g  and random noises were white Gaussian noise with zero mean and standard deviation of 50 g  .…”

Section: A Simulation Settingmentioning

confidence: 99%

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

et al. 2021

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“…In this study, bias estimation was also performed for the accelerometer by obtaining an acceleration estimate that considers the posture. For acceleration, the accelerometer bias estimation using zero velocity update has been performed for underwater vehicles [ 12 ], but it is difficult to perform such estimation for constantly moving ships.…”

Section: Introductionmentioning

confidence: 99%

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

Fukuda

Kubo

2022

Sensors

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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.

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“…It has then been used in positioning applications to describe the uncertainty of the system model and statistics characteristic of observation noise. Yao et al [ 28 ] applied an IMM-aided zero velocity update (ZUPT) technology for an INS/DVL integrated navigation system to mitigate the navigation error during the pure INS mode. Further, Yao et al [ 29 ] proposed the IMM-UKF-aided SINS and ultra-short baseline (USBL) calibration solution.…”

Section: Introductionmentioning

confidence: 99%

A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment

Yang

Gao

Huang

et al. 2022

Sensors

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Navigation and positioning of autonomous underwater vehicles (AUVs) in the complex and changeable marine environment are crucial and challenging. For the positioning of AUVs, the integrated navigation of the strap-down inertial navigation system (SINS), Doppler velocity log (DVL), and pressure sensor (PS) has a common application. Nevertheless, in the complex underwater environment, the DVL performance is affected by the current and complex terrain environments. The outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. To address these issues, in this paper, a novel tightly integrated navigation model of the SINS, DVL, and PS is established. In contrast to the traditional SINS, DVL, and PS tightly integrated navigation methods, the proposed method in this paper is based on the velocity variation of the DVL beam by applying the DVL bottom-track and water-track models. Furthermore, a new robust interacting multiple models (RIMM) information fusion algorithm is proposed. In this algorithm, DVL beam anomaly is detected, and the Markov transfer probability matrix is accordingly updated to enable quick model matching. By simulating the motion of the AUV in a complex underwater environment, we also compare the performance of the traditional loosely integrated navigation (TLIN) model, the tightly integrated navigation (TTIN) model, and the IMM algorithm. The simulation results show that because of the PS, the velocity and height in the up-change amplitude of the four algorithms are small. Compared with the TLIN algorithm in terms of maximum deviation of latitude and longitude, the RIMM algorithm also improves the accuracy by 39.1243 m and 26.4364 m, respectively. Furthermore, compared with the TTIN algorithm, the RIMM algorithm improves latitude and longitude accuracy by 1.8913 m and 11.8274 m, respectively. A comparison with IMM also shows that RIMM improves the accuracy of latitude and longitude by 1.1506 m and 7.2301 m, respectively. The results confirm that the proposed algorithm suppresses the observed noise and outliers of DVL and further achieves quick conversion between different DVL models while making full use of the effective information of the DVL beams. The proposed method also improves the navigation accuracy of AUVs in complex underwater environments.

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

Cited by 29 publications

References 21 publications

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

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

A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment

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