Design and practical implementation of kinematic constraints in Inertial Navigation System-Doppler Velocity Log (INS-DVL)-based navigation

Karmozdi, Ali; Hashemi, Mojtaba; Salarieh, Hassan

doi:10.1002/navi.271

Cited by 22 publications

(15 citation statements)

References 18 publications

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“…This section discusses the mathematical modeling of the underwater vehicle taken from [ 40 , 41 , 42 , 43 , 44 ]. The first subsection explains the notations used for modeling.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Moreover, this configuration requires an initial condition for position, velocity, and attitude. The kinematics equations for the underwater vehicles used in this work are described in [ 42 , 43 , 44 ]. The rate of change in position

and velocity

of vehicle in

frame is related by the following differential equation:

…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Equation (13) shows a quaternion representation of the roll, pitch, and yaw angles of the vehicle. Interested readers can find more details for mathematical modeling of an underwater vehicle in starpdown configuration in [ 40 , 41 , 42 , 43 , 44 ].…”

Section: Mathematical Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Shaukat

Ahmed

Iqbal

et al. 2021

Sensors

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The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.

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“…This section discusses the mathematical modeling of the underwater vehicle taken from [ 40 , 41 , 42 , 43 , 44 ]. The first subsection explains the notations used for modeling.…”

Section: Mathematical Modelingmentioning

confidence: 99%

and velocity

of vehicle in

frame is related by the following differential equation:

…”

Section: Mathematical Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Shaukat

Ahmed

Iqbal

et al. 2021

Sensors

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show abstract

“…Moreover,˜∈ R 3×1 is the vector of the perturbed quaternion; ∈ R 3×1 and ∈ R 3×1 represent the three-dimensional accelerometer and gyro biases in the body frame, respectively. For the definition of frames, refer to Karmozdi et al (2018).…”

Section: Ins/dvl Integrated Navigation Systemmentioning

confidence: 99%

New fault detection and fault-tolerant scheme for Doppler velocity logger outage in ocean navigation systems

Hashemi

Shami

2021

J. Navigation

Self Cite

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The inertial navigation system/Doppler velocity logger (INS/DVL) plays an important role in ocean navigation. Any DVL malfunction poses serious risks to navigation. A precise detection system is required to detect the initial moments of DVL signal malfunctions; moreover, with loss of DVL, a fault-tolerant scheme (FTS) is necessary for DVL signal reconstruction. In this paper, an evolutionary knowledge-based method, namely improved evolutionary TS-fuzzy (I-eTS), is adopted to build an artificial intelligence (AI)-based pseudo DVL to deal with long-term outage of DVL. By employing Gaussian process regression (GPR) models for fault detection, a new FTS is constructed. To verify the effectiveness of the new fault detection and fault tolerance system, navigation data is gathered by a test setup and algorithms are performed in the laboratory. In the tests, it is demonstrated that the proposed FTS leads to rapid detection of both gradual and abrupt faults, which leads to less interaction between fault detection and FTS.

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“…The dynamic model of plants, which describes the relationship between the plant motion, control input, and surrounding environment, is fused with navigation sensors, improving the navigation accuracy and reliability. The dynamic model-aided navigation method has been used for aircraft [12], vehicles [13], and underwater robots [14]. The fusion schemes for different plants are different because they should be designed based on the model characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

Lyu

Wang

Lai

et al. 2019

Sensors

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In this paper, a drag model-aided fault-tolerant state estimation method is presented for quadrotors. Firstly, the drag model accuracy was improved by modeling an angular rate related item and an angular acceleration related item, which are related with flight maneuver. Then the drag model, light detection and ranging (LIDAR), and inertial measurement unit (IMU) were fused based on the Federal Kalman filter frame. In the filter, the LIDAR estimation fault was detected and isolated, and the disturbance to the drag model was estimated and compensated. Some experiments were carried out, showing that the velocity and position estimation were improved compared with the traditional LIDAR/IMU fusion scheme.

show abstract

Design and practical implementation of kinematic constraints in Inertial Navigation System-Doppler Velocity Log (INS-DVL)-based navigation

Cited by 22 publications

References 18 publications

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

New fault detection and fault-tolerant scheme for Doppler velocity logger outage in ocean navigation systems

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

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