A Strapdown Interial Navigation System/Beidou/Doppler Velocity Log Integrated Navigation Algorithm Based on a Cubature Kalman Filter

Gao, Wei; Zhang, Ya; Wang, Jianguo

doi:10.3390/s140101511

Cited by 52 publications

(27 citation statements)

References 15 publications

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“…Thus, the model precision can reach third-order or higher [12]. Furthermore, compared with EKF and UKF, CKF does not demand calculating the Jacobians' or Hessians' matrix, so that the computational complexity will be alleviated to a certain extent.…”

Section: Nonlinear Filter Ckfmentioning

confidence: 99%

“…Since the CKF is not only simple and easy to implement, but also has high precision and good convergency, it is widely used in nonlinear estimations [12,21,28].…”

Section: Nonlinear Filter Ckfmentioning

confidence: 99%

“…Hu et al [11] proposed modifying the error equations of the MEMS inertial measurement system to satisfy the filtering model, and Gao proposed a new alignment method based on the interacting multiple model (IMM) algorithm, considering that one model cannot accurately describe the practical system. As we all know that almost all actual systems are nonlinear, especially when AUVs are in a high maneuvering state or misalignment angles are large, using the classical linear filter will introduce errors inevitably [12,13]. Therefore, the nonlinear filter should be considered in initial alignment for AUVs' SINS.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

Dong

Sun

et al. 2017

Symmetry

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Abstract:As a typical navigation system, the strapdown inertial navigation system (SINS) is crucial for autonomous underwater vehicles (AUVs) since the SINS accuracy determines the performance of AUVs. Initial alignment is one of the key technologies in SINS, and initial alignment time and initial alignment accuracy affect the performance of SINS directly. As actual systems are nonlinear, the nonlinear filter is widely used to improve the accuracy of the initial alignment. Due to its higher precision and lower computational load, the cubature Kalman filter (CKF) has done well in state estimation. However, the noise characteristics need to be known exactly as prior knowledge, which is difficult or even impossible to achieve. Thus, the adaptive filter should be introduced in the initial alignment algorithm to suppress the uncertainty effect caused by the unknown system noise. Therefore, taking the nonlinearity and uncertainty into account, a novel initial alignment algorithm for AUVs is proposed in this manuscript, based on CKF and the adaptive variance components estimation (VCE) filter (VCKF). Additionally, the simulation and experiment results show that not only the accuracy, but also the convergence speed can be improved with this proposed method. The validity and superiority of this novel adaptive initial alignment algorithm based on VCKF are verified.

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Section: Nonlinear Filter Ckfmentioning

confidence: 99%

“…Since the CKF is not only simple and easy to implement, but also has high precision and good convergency, it is widely used in nonlinear estimations [12,21,28].…”

Section: Nonlinear Filter Ckfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

Dong

Sun

et al. 2017

Symmetry

Self Cite

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“…By using a Kalman filter (KF), the highly accurate velocities provided by the DVL can be used to effectively restrain the error accumulation of the SINS [15,16]. However, the dependency of the DVL signal on the acoustic environment may occasionally make the DVL malfunction [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Zhu

Cheng

et al. 2017

Applied Sciences

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Abstract:As a common device for underwater integrated navigation systems, Doppler velocity log (DVL) has the risk of malfunction. To improve the reliability of navigation systems, a hybrid approach is presented to forecast the measurements of the DVL while it malfunctions. The approach employs partial least squares regression (PLSR) coupled with support vector regression (SVR) to build a hybrid predictor. As the current and past calculating velocities of strapdown inertial navigation system (SINS) are taken as the predictor's inputs, PLSR is applied to cope with the situation where there exists intense relativity among independent variables. Since PLSR is a linear regression, SVR is used to predict the residual components of the PLSR prediction to improve the accuracy. When the DVL works well, the hybrid predictor is trained online as a backup, whereas during malfunctions, the predictor offers the estimation of the DVL measurements for information fusion. The performance of the proposed approach is verified with simulations based on SINS/DVL/MCP/pressure sensor (PS) integrated navigation system. The comparison results indicate that the PLSR-SVR hybrid predictor can correctly provide the estimated DVL measurements and effectively extend the tolerance time on DVL malfunctions, thereby improving the navigation accuracy and reliability.

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“…In another study (Benzerrouk et al, 2013), an adaptive cubature INS/GNSS integrated navigation system was presented for aerospace applications and evaluated in simulations. Gao et al (2014) also proposed a SINS-BeiDou-DVL integrated navigation system based on a CKF for marine applications and its performance was examined through numerical simulations.…”

mentioning

confidence: 99%

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

Shabani

2015

J. Navigation

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In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.2. Underwater Integrated Navigation System. 3. Error State Kalman Filter.4. Unscented Filtering.

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A Strapdown Interial Navigation System/Beidou/Doppler Velocity Log Integrated Navigation Algorithm Based on a Cubature Kalman Filter

Cited by 52 publications

References 15 publications

An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

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