Kalman-filter-based time-varying parameter estimation via retrospective optimization of the process noise covariance

2016 ◽  
Author(s):  
Frantisek M. Sobolic ◽  
Dennis S. Bernstein
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Time Varying ◽  
Process Noise ◽  
Time Varying Parameter ◽  
Noise Covariance

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MODEL TIME VARYING PARAMETER (TVP)

2016 ◽  
Vol 5 (3) ◽  
pp. 117
Author(s):  
I PUTU GEDE DIAN GERRY SUWEDAYANA ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Percentage Error ◽  
Time Varying ◽  
Mean Square ◽  
Explanatory Variables ◽  
Space Model ◽  
A Value ◽  
Time Varying Parameter ◽  
Kalman Filter Algorithm

The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP) model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE) is equal to dan root mean square percentage error (RMSPE) is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016).

Online Bayesian Time-varying Parameter Estimation of HIV-1 Time-series*

2012 ◽  
Vol 45 (16) ◽  
pp. 1294-1299 ◽  
Author(s):  
András Hartmann ◽  
Susana Vinga ◽  
João M. Lemos
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Time Varying ◽  
Time Varying Parameter ◽  
Hiv 1

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 Novel Process Noise Model for GNSS Kalman Filter Based on Sensitivity Analysis of Covariance with Poor Satellite Geometry

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6056
Author(s):  
Yoji Takayama ◽  
Takateru Urakubo ◽  
Hisashi Tamaki
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Conventional Technique ◽  
Urban Environments ◽  
Analysis Of Covariance ◽  
Noise Model ◽  
Process Noise ◽  
Satellite Geometry ◽  
Noise Covariance ◽  
Filter Divergence

One of the great unsolved GNSS problems is inaccuracy in urban canyons due to Non-Line-Of-Sight (NLOS) signal reception. Owing to several studies about the NLOS signal rejection method, almost all NLOS signals can be excluded from the calculation of the position. However, such precise NLOS rejection would make satellite geometry poor, especially in dense urban environments. This paper points out, through numerical simulations and theoretical analysis, that poor satellite geometry leads to unintentional performance degradation of the Kalman filter with a conventional technique to prevent filter divergence. The conventional technique is to bump up process noise covariance, and causes unnecessary inflation of estimation-error covariance when satellite geometry is poor. We propose a novel choice of process noise covariance based on satellite geometry that can reduce such unnecessary inflation. Numerical and experimental results demonstrate that performance improvement can be achieved by the choice of process noise covariance even for a poor satellite geometry.

Linear Structure Vibration Control Using MFT and Robust Kalman-Filter-Based OMFC Method

1999 ◽  
Vol 15 (1) ◽  
pp. 1-9
Author(s):  
Shinn-Horng Chen ◽  
Jyh-Horng Chou
Keyword(s):  
Kalman Filter ◽  
Robust Stability ◽  
Stability Criterion ◽  
System Response ◽  
Time Varying ◽  
Structure System ◽  
Mechanical Structure ◽  
Model Following Control ◽  
Time Varying Parameter ◽  
Robust Kalman Filter

ABSTRACTThis paper proposes a robust Kalman-filter-based optimal model-following control (OMFC) methodology for actively suppressing the vibration of the mechanical structure system, which is modeled by the modal force technique (MFT) and subjects to both disturbance/noise uncertainties and linear structured time-varying parameter perturbations. The proposed method can not only avoid the problem of how to choose appropriate weighting matrices in the quadratic cost function of the linear quadratic Gaussian (LQG) control but also make the controlled closed-loop system to have desired system response characteristics. Besides, this paper also presents a robust stability criterion to guarantee that the designed controller can keep the controlled mechanical structure system from the possibility of time-varying-parameter-perturbation-induced instability. An example of L-shaped cantilever beam structure system is employed to demonstrate the application of the proposed method and the use of the robust stability criterion. Numerical simulation is performed to evaluate the improvement of the vibration response.

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