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

Gao, Kang; Ren, Shenqiang; Yi, Guoxing; Zhong, Jiapeng; Wang, Zhenhuan

doi:10.3390/s18113896

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…In addition, the quaternions’ kinematics is given by Gao et al. 21 where ω=ωrb b is the angular velocity of body frame relative to reference frame, expressed in the b-frame. Because of the previous definitions, one can now write the following important equation 24 …”

Section: Quaternion Representationmentioning

confidence: 99%

“…New mechanization in the pseudo-geographic frame is discussed in Liu et al 18 that eliminates the effect of linear movement errors on the heading's error by decoupling. Using improved robust Huber Cubarure Kalman filter, incremental predictive Kalman filter and adaptive cubature Kalman filtering are addressed in Zhang et al, 19 Fathi et al, 20 and Gao et al, 21 respectively.…”

Section: Introductionmentioning

confidence: 99%

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A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems

Ghanbarpourasl

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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A new robust quaternion Kalman filter is developed for accurate alignment of stationary strapdown inertial navigation system. Most fine alignment algorithms have tried to estimate the biases of gyroscopes and accelerometers to reduce the errors of the alignment process. In stationary platforms, due to fixed inputs for sensors, the summation of various errors such as fixed bias, misalignment, scale factor, and nonlinear errors acts like one bias error, and then the identification of each error will be impossible. The observability of gyros and accelerometers’ biases has also been studied. But, nowadays, we know that all of these unknown parameters are not observable. Then this problem can increase the complication of the alignment algorithm. The accelerometers’ errors mainly affect the errors of the roll and pitch angles, but a big portion of the heading’s error results from the gyroscopes’ errors. Modeling of all errors as additional states without considering the observability parameters has no benefits, but will increase the filter’s dimension, so the filter’s performance will decrease. In this study, due to the observability problem, a new robust multiplicative quaternion Kalman filter is designed for the alignment of a stationary platform. The presented algorithm does not estimate the sensors’ errors, but it is robust to uncertainty in the sensors’ errors. In the proposed scheme, the bounds of parameters’ errors are introduced to filter, and the filter tries to remain robust with respect to these uncertainties. The method uses the benefits of quaternions in attitude modeling, and then the robust filter is adapted to work with quaternions. The ability of the new algorithm is evaluated with MATLAB simulations. The outcomes show that the presented algorithm is more accurate than other traditional methods. The extended Kalman filter with accelerometers’ outputs and the horizontal velocities as the measurement equations and additive quaternion Kalman filter are used for comparisons.

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Section: Quaternion Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems

Ghanbarpourasl

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The time update of the iterative operation is described as shown in Equation (9). Combining the Sage-Husa filtering algorithm [58][59][60] with the UKF approach, the adaptive filtering method is introduced to correct the process noise variance, in which the measured noise variance in the formula real timely and the improved time-varying noise characteristics are applied to suit the nonlinearity. The adaptive filter can be used to obtain the measurement and updated data onto the iterative algorithm as shown in Equation (10).…”

Section: Collaborative Estimationmentioning

confidence: 99%

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

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The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

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“…But the alignment accuracy is not high in a complex environment. In [23], Gao et al proposed an improved ACKF-KF method, which can complete the initial alignment well in the case of unknown measurement noise. But the method does not work well for the alignment of ships at sea.…”

Section: Introductionmentioning

confidence: 99%

An Alignment Method Based on KF-ASMUKF Hybrid Filtering for Ship’s SINS under Mooring Conditions

Yao

Yang

Peng

2021

Sensors

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To solve the problem that the ship’s strapdown inertial navigation system (SINS) alignment accuracy decreases with the increase of the nonlinear filtering state dimension under mooring conditions, a method based on Kalman filter (KF) and Adaptive scale mini-skewness single line sampling Unscented Kalman Filter (ASMUKF) hybrid filtering algorithm is proposed in this paper. Three improvements are made as the following: (1) adopt a new sampling strategy. To obtain the ASMUKF filtering algorithm, scale mini-skewness single line sampling is used to replaced the traditional symmetrical sampling method and an adaptive scale factor is adapted into the Unscented Kalman Filter (UKF) to correct the real-time transformation sampling process; (2) the improved ASMUKF algorithm is combined with KF to form KF-ASMUKF hybrid filtering model; (3) the hybrid filtering model is divided into linear and nonlinear parts. The linear filtering part adopts the KF filtering model and the nonlinear filtering part adopts the ASMUKF model. Then, the calculation steps of the hybrid filtering algorithm is designed in this paper. The simulation and experimental results show that the hybrid filtering algorithm proposed has certain advantages over the traditional algorithm, and it can reduce the ship’s SINS calculation amount and improve alignment accuracy under mooring conditions.

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

Cited by 12 publications

References 30 publications

A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems

A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

An Alignment Method Based on KF-ASMUKF Hybrid Filtering for Ship’s SINS under Mooring Conditions

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