A new Gaussian–Student's <i>t</i> mixing distribution‐based Kalman filter with unknown measurement random delay rate

Shan, Chenghao; Zhou, Weidong; Yang, Yefeng; Shan, Hanyu

doi:10.1002/acs.3417

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Through simulation experiments, it has been proven that the proposed filter has better performance and estimation accuracy in dealing with the nonlinear filtering problem of NSHTMN [15,16]. Huang et al proposed a novel robust GSTM distribution Kalman filter, which can adapt to NSHTMN by using adaptive learning of mixed probabilities, thereby improving estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

Lan,

Wang,

Song

et al. 2024

Meas. Sci. Technol.

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The error characteristics of a typical commercial MEMS-IMU were analyzed. Using 15 ICM-42688 sensors, a 3*5 MEMS-IMU array was designed, and a weighted data fusion method based on Allan variance for the MEMS-IMU array was proposed. This effectively reduces the random measurement errors of the MEMS-IMU, providing a data foundation for the precise measurement of motion and attitude of robots, vehicles, aircraft, and other systems under low-cost conditions. The text describes a measurement method for non-stationary motion attitudes of sports vehicles based on adaptive Kalman-Mahony. Specifically, it first uses adaptive Kalman filtering on array sensor data to calculate the measurement noise in real-time and adaptively adjust the filtering gain. Then, it determines the compensation coefficient of the accelerometer to the gyroscope angular velocity based on the motion state of the vehicle, and solves the attitude through complementary filtering to obtain the motion attitude quaternion. Finally, it converts it into the roll angle, pitch angle, and yaw angle of the sports vehicle. Experimental results show that the proposed MEMS-IMU array weighted data fusion method based on Allan variance has significant advantages over traditional single MEMS-IUM methods and traditional average weighting methods in reducing sensor angle random walk and zero drift instability. The proposed adaptive Kalman-Mahony attitude measurement method also shows a significant improvement in the accuracy of non-stationary motion attitude measurement compared to traditional methods.

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Section: Introductionmentioning

confidence: 99%

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

Lan,

Wang,

Song

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In [19], a risk-sensitive KF (RSKF) is proposed by minimizing the expectation of the accumulated exponential quadratic error for system with one-step randomly delayed measurement with unknown probability and uncertain model. In recent years, the use of variational Bayesian (VB) method to identify unknown parameters has been widely studied [20][21][22][23], such as the identification of Student's t noise parameters [24][25][26]. In [27], an improved KF with one-step randomly delayed measurement is proposed.…”

Section: Introductionmentioning

confidence: 99%

VB-Based Gaussian Sum Cubature Kalman Filter for Adaptive Estimation of Unknown Delay and Loss Probability

Wang,

Zhang

2024

International Journal of Aerospace Engineering

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The traditional Kalman filter assumes that all measurements can be obtained in real time, which is invalid in practical engineering. Therefore, a variational Bayesian- (VB-) based Gaussian sum cubature Kalman filter is proposed to solve the nonlinear tracking problem of multistep random measurement delay and loss (MRMDL) with unknown probability. First, the measurement model with MRMDL is modified by Bernoulli random variables. Then, the expression of the likelihood function is reformulated as a mixture of multiple Gaussian distributions, and the cubature rule is used to improve the estimation accuracy under the framework of Gaussian sum filter in the process of time update. Finally, by constructing a hierarchical Gaussian model, the unknown and time-varying measurement delay and loss probability are estimated in real time with the state jointly using the VB method in the measurement update stage. The algorithm does not need to calculate the equivalent noise covariance matrix so as to avoid the possible division by zero operation, which improves the stability of the algorithm. Simulation results for a target tracking problem show that the proposed algorithm has a better performance in the presence of MRMDL and can estimate the unknown measurement delay and loss probability accurately.

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Gaussian–Student’s t Mixture Distribution-Based Robust Kalman Filter for Global Navigation Satellite System/Inertial Navigation System/Odometer Data Fusion

Wu,

Jiang,

Tang

et al. 2024

Remote Sensing

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Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively.

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A new Gaussian–Student's t mixing distribution‐based Kalman filter with unknown measurement random delay rate

Cited by 3 publications

References 30 publications

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

VB-Based Gaussian Sum Cubature Kalman Filter for Adaptive Estimation of Unknown Delay and Loss Probability

Gaussian–Student’s t Mixture Distribution-Based Robust Kalman Filter for Global Navigation Satellite System/Inertial Navigation System/Odometer Data Fusion

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