Underwater Doppler Navigation with Self-calibration

Pan, Xianfei; Wu, Yuanxin

doi:10.1017/s0373463315000703

Cited by 42 publications

(22 citation statements)

References 21 publications

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“…A Global Navigation Satellite Systems (GNSS), most frequently the Global Positioning System (GPS), providing velocity and position directly in the geographic coordinate system, is widely used as an aiding source for in-motion alignment (Jiang et al, 2013; Kaygısız and Sen, 2015; Kubo et al, 2006; Wu and Pan, 2013a; 2013b). However, GNSS are vulnerable to jamming, blocking and spoofing, greatly affecting their availability and reliability (Chang et al, 2016; Pan and Wu, 2016; Liu et al, 2018). For the land vehicles of interest here, an odometer is the most commonly used velocity-aiding source for autonomous initial alignment (Xu et al, 2017; Bimal and Joshi, 2015; Wu, 2014).…”

Section: Introductionmentioning

confidence: 99%

Autonomous In-motion Alignment for Land Vehicle Strapdown Inertial Navigation System without the Aid of External Sensors

Yang

Liu

et al. 2018

J. Navigation

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This paper describes a fully autonomous real-time in-motion alignment algorithm for Strapdown Inertial Navigation Systems (SINS) in land vehicle applications. Once the initial position is available, the vehicle can start a mission immediately with accurate attitude, position and velocity information determined within ten minutes. This is achieved by two tightly coupled stages, that is, real-time Double-vector Attitude Determination Coarse Alignment (DADCA) and Backtracking Fine Alignment (BFA). In the DADCA process, the vehicle motion is omitted to roughly estimate the attitude at the very start of the alignment. Meanwhile, attitude quaternions and velocity increments are extracted and recorded. The BFA process utilises the stored data and exploits the Non-Holonomic Constraints (NHC) of a vehicle to obtain virtual velocity measurements. A linear SINS/NHC Kalman filter with mounting angles as extended states is constructed to improve the fine alignment accuracy. The method is verified by three vehicle tests, which shows that the accuracy of alignment azimuth is 0·0358° (Root Mean Square, RMS) and the positioning accuracy is about 15 m (RMS) at the end of the alignment.

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Section: Introductionmentioning

confidence: 99%

Autonomous In-motion Alignment for Land Vehicle Strapdown Inertial Navigation System without the Aid of External Sensors

Yang

Liu

et al. 2018

J. Navigation

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“…If the carrier has no attitude maneuver, then

are small amounts. Equation (15) can be simplified as [ 19 ]:

…”

Section: Formulate Problemmentioning

confidence: 99%

“…The Kalman filter is widely used in initial alignment [ 16 , 17 ]. In [ 18 , 19 ], Kalman filter-based initial alignment for SINS/Doppler velocity log (DVL) integration is studied. However, the Kalman filter requires knowledge of the noise statistics and a roughly known initial attitude that is hardly achieved when the vehicle running freely.…”

Section: Introductionmentioning

confidence: 99%

An Optimization-Based Initial Alignment and Calibration Algorithm of Land-Vehicle SINS In-Motion

Gao

Ren

Chen

et al. 2018

Sensors

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For a running freely land-vehicle strapdown inertial navigation system (SINS), the problems of self-calibration and attitude alignment need to be solved simultaneously. This paper proposes a complete alignment algorithm for the land vehicle navigation using Inertial Measurement Units (IMUs) and an odometer. A self-calibration algorithm is proposed based on the global observability analysis to calibrate the odometer scale factor and IMU misalignment angle, and the initial alignment and calibration method based on optimal algorithm is established to estimate the attitude and other system parameters. This new algorithm has the capability of self-initialization and calibration without any prior attitude and sensor noise information. Computer simulation results show that the performance of the proposed algorithm is superior to the extended Kalman filter (EKF) method during the oscillating attitude motions, and the vehicle test validates its advantages.

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“…Unlike the alignment on the static base, the alignment on moving base calls for the carrier motion information provided by the external device [ 8 , 9 ]. The GPS is commonly onboard navigation equipment, which can provide high accuracy navigation information [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved ACKF/KF Initial Alignment Method for Odometer-Aided Strapdown Inertial Navigation System

Gao

Ren

et al. 2018

Sensors

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For a land-vehicle strapdown inertial navigation system (SINS), the problem of initial alignment with large misalignment angle in-motion needs to be solved urgently. This paper proposes an improved ACKF/KF initial alignment method for SINS aided by odometer. The SINS error equation with large misalignment angle is established first in the form of an Euler angle. The odometer/gyroscope dead reckoning (DR) error equation is deduced, which makes the observation equation linear when the position is taken as the observation of the Kalman filter. Then, based on the cubature Kalman filter, the Sage-Husa adaptive filter and the characteristics of the observation equation, an improved ACKF/KF method is proposed, which can accomplish initial alignment well in the case of unknown measurement noise. Computer simulation results show that the performance of the proposed ACKF/KF algorithm is superior to EKF, CKF and AEKF method in accuracy and stability, and the vehicle test validates its advantages.

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Underwater Doppler Navigation with Self-calibration

Cited by 42 publications

References 21 publications

Autonomous In-motion Alignment for Land Vehicle Strapdown Inertial Navigation System without the Aid of External Sensors

Autonomous In-motion Alignment for Land Vehicle Strapdown Inertial Navigation System without the Aid of External Sensors

An Optimization-Based Initial Alignment and Calibration Algorithm of Land-Vehicle SINS In-Motion

An Improved ACKF/KF Initial Alignment Method for Odometer-Aided Strapdown Inertial Navigation System

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