scholarly journals Online Estimation of ARW Coefficient of Fiber Optic Gyro

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Baiqing Hu ◽  
Fangjun Qin ◽  
Kailong Li
Keyword(s):  
Kalman Filter ◽  
Fiber Optic ◽  
Noise Analysis ◽  
Digital Simulation ◽  
State Space Model ◽  
Allan Variance ◽  
Measurement Noise ◽  
Fiber Optic Gyro ◽  
Online Estimation ◽  
Measurement Noise Covariance

As a standard method for noise analysis of fiber optic gyro (FOG), Allan variance has too large offline computational burden and data storages to be applied to online estimation. To overcome the barriers, the state space model is firstly established for FOG. Then the Sage-husa adaptive Kalman filter (SHAKF) is introduced in this field. Through recursive calculation of measurement noise covariance matrix, SHAKF can avoid the storage of large amounts of history data. However, the precision and stability of this method are still the primary matters that needed to be addressed. Based on this point, a new online method for estimation of the coefficient of angular random walk is proposed. In the method, estimator of measurement noise is constructed by the recursive form of Allan variance at the shortest sampling time. Then the estimator is embedded into the SHAKF framework resulting in a new adaptive filter. The estimations of measurement noise variance and Kalman filter are independent of each other in this method. Therefore, it can address the problem of filtering divergence and precision degrading effectively. Test results of both digital simulation and experimental data of FOG verify the validity and feasibility of the proposed method.

Research on Random Errors of Fiber Optic Gyro Based on Dynamic Allan Variance and Algorithm Improvement

2015 ◽  
Vol 35 (4) ◽  
pp. 0406003 ◽  
Author(s):  
张谦 Zhang Qian ◽  
王玮 Wang Wei ◽  
王蕾 Wang Lei ◽  
高鹏宇 Gao Pengyu
Keyword(s):  
Fiber Optic ◽  
Allan Variance ◽  
Random Errors ◽  
Fiber Optic Gyro

Autonomous estimation of Allan variance coefficients of onboard fiber optic gyro

2011 ◽  
Vol 6 (09) ◽  
pp. P09005-P09005 ◽  
Author(s):  
Ningfang Song ◽  
Rui Yuan ◽  
Jing Jin
Keyword(s):  
Fiber Optic ◽  
Allan Variance ◽  
Fiber Optic Gyro

Passive Location of Angle Sensors Using Extend Square-Root Cubature Kalman Filter

2011 ◽  
Vol 143-144 ◽  
pp. 577-581 ◽  
Author(s):  
Yang Zhang ◽  
Guo Sheng Rui ◽  
Jun Miao
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Measurement Noise ◽  
Filter Method ◽  
Square Root ◽  
Passive Location ◽  
Mode Decomposition ◽  
Measurement Noise Covariance ◽  
Cubature Kalman Filter ◽  
Noise Covariance

A new nonlinear filter method Cubature Kalman Filter (CKF) is improved for passive location with moving angle-measured sensors’ measurements.Firstly,it used Empirical Mode Decomposition (EMD) algorithm to estimate measurement noise covariance; And then the covariance of the procession noise and measurement noise is brought into the circle; Meanwhile,CKF is improved by the way of square root to keep its stability and positivity,and the results of track by Extend SCKF are compared with the results by Unscented Kalman Filter (UKF) in the text;By the tracking results to the velocity of the target, Extend SCKF algorithm can not only track the target with unknown measurement noise but also improve the passive position precision remarkably as the same difficulty as UKF.

scholarly journals Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

2020 ◽  
Author(s):  
Chenghao Shan ◽  
Weidong Zhou ◽  
Yefeng Yang ◽  
Zihao Jiang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Measurement Noise ◽  
Time Varying ◽  
Monitoring Strategy ◽  
Adaptive Kalman Filter ◽  
Variational Bayesian ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Fading Factor

Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

Kalman Filter and Complementary Filter in Attitude Estimation of APM

2014 ◽  
Vol 577 ◽  
pp. 794-797 ◽  
Author(s):  
Feng Lin ◽  
Xi Lan Miao ◽  
Xiao Guang Qu
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Attitude Estimation ◽  
Measurement Noise ◽  
Experimental Results ◽  
Extend Kalman Filter ◽  
Noise Covariance Matrix ◽  
Complementary Filter ◽  
Measurement Noise Covariance ◽  
Noise Covariance

This paper presents the results of a quaternion based extend Kalman filter (EKF) and complementary filter for ArduPilotMega (APM) attitude estimation. In addition, a new method to get the measurement noise covariance matrix R is proposed. Experimental results show that the two algorithms can meet the requirements, but the complementary filter can yield better performance than EKF.

scholarly journals Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5808
Author(s):  
Dapeng Wang ◽  
Hai Zhang ◽  
Baoshuang Ge
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Stochastic Systems ◽  
Unscented Kalman Filter ◽  
Minimum Variance ◽  
Measurement Noise ◽  
Time Varying ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Sensor Information

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.

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