Improved Measurement Noise Covariance Estimation for N-channel Feedback Cancellation Based on the Frequency Domain Adaptive Kalman Filter

2019 ◽  
Author(s):  
Jan Franzen ◽  
Tim Fingscheidt
Keyword(s):  
Kalman Filter ◽  
Frequency Domain ◽  
Covariance Estimation ◽  
Measurement Noise ◽  
Adaptive Kalman Filter ◽  
Feedback Cancellation ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Channel Feedback

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.

scholarly journals Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 198
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 of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of multi-fading factor and an updated monitoring strategy adaptive Kalman filter-based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model and 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 updated 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.

scholarly journals Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bingbing Gao ◽  
Gaoge Hu ◽  
Wenmin Li ◽  
Yan Zhao ◽  
Yongmin Zhong
Keyword(s):  
Kalman Filter ◽  
Maximum Likelihood ◽  
Sequential Quadratic Programming ◽  
Covariance Estimation ◽  
Measurement Noise ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Measurement Noise Covariance ◽  
Cubature Kalman Filter ◽  
Noise Covariance

With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a promising navigation and positioning strategy. However, due to the nonlinear propagation characteristic of INS error and inevitable involvement of inaccurate measurement noise statistics, it is difficult to achieve the optimal solution through the INS/BDS integration. This paper proposes a method of cubature Kalman filter (CKF) with the measurement noise covariance estimation by using the maximum likelihood principle to solve the abovementioned problem. It establishes an estimation model for measurement noise covariance according to the maximum likelihood principle, and then, its estimation is calculated by utilizing the sequential quadratic programming. The estimated measurement noise covariance will be fed back to the procedure of CKF to improve its adaptability. Simulation and comparison analysis verify that the proposed method can accurately estimate measurement noise covariance to effectively restrain its influence on navigation solution, leading to improved navigation performance for the INS/BDS integration.

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.

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.

The Selection of Process Noise Covariance Q and Measurement Noise Covariance R on Kalman Filter

2014 ◽  
Vol 513-517 ◽  
pp. 4342-4345 ◽  
Author(s):  
Chao Yi Wei ◽  
Shu Jian Ye ◽  
Xu Guang Li ◽  
Mei Zhi Xie ◽  
Feng Yan Yi
Keyword(s):  
Kalman Filter ◽  
Simulation Model ◽  
Dynamic Model ◽  
Measurement Noise ◽  
State Estimator ◽  
Process Noise ◽  
Measurement Noise Covariance ◽  
High Measurement ◽  
Noise Covariance ◽  
Selection Of

At first, a seven degree of freedom dynamic model of tractor semi-trailer was established, and a simulation model with the Matlab/Simulink software was established too. Based on the model, a state estimator based on Kalman theory was designed, using easily measured parameters to estimate the parameters that are difficultly measured or have high measurement cost, after that, compared estimated values and simulation measurements and analyzed the influence with changes of process noise covariance Q and measurement noise covariance R.

An Adaptive & Fault Tolerant SINS/GPS Integrated Navigation Scheme Robustified against Slowly Growing Errors in GPS Updates

2013 ◽  
Vol 390 ◽  
pp. 500-505 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng ◽  
Jamshaid Ali
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Random Errors ◽  
Actual Measurement ◽  
Integrated Navigation System ◽  
System Noise ◽  
Measurement Noise Covariance ◽  
Noise Covariance

The Strapdown Inertial Navigation System (SINS) renders excellent attitude, position and velocity solutions on short term basis, but when used as stand-alone navigation system, its accuracy deteriorates with the passage of time. On the other hand GPS has long-standing stability with a consistent precisiongenerally having only bounded random errors in position and velocity. Integrated navigation system is used to augment the complementary features of SINS and GPS. In integrated navigation system external fixes for position and/or velocity and/or attitude are used to contain the growing errors of SINS. Kalman filter is generally used as integration tool for integrated navigation system. Kalman filter algorithm is based on the assumptions that the system model and the measurement models are linear and the system random errors and measurement random errors are Gaussian in nature expressed with fixed covariances. But in real navigation systems these assumptions are seldom fulfilled and hence Kalman filter renders unsatisfactory results. Adaptive Kalman filter provides the solution to the problem by adjusting the system noise covariance and measurement noise covariance in real time in the light of actual measurement errors or actual dynamics of thevehicle. In this paper an innovation and residual based adaption of measurement noise covariance and system noise covariance is presented. The presented scheme has been applied on an SINS/GPS Integrated Navigation Systemand it has been validated that the scheme provide significantly better results as compared to standard Kalman filter on occurrence slowly growing errors as well as excessive random errors in GPS measurements.

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