scholarly journals Innovation-based fractional order adaptive Kalman filter

2020 ◽  
Vol 71 (1) ◽  
pp. 60-64
Author(s):  
Ravi Pratap Tripathi ◽  
Ashutosh Kumar Singh ◽  
Pavan Gangwar
Keyword(s):  
Kalman Filter ◽  
Fractional Order ◽  
Adaptive Estimation ◽  
Lyapunov Theory ◽  
Measurement Noise ◽  
Position Tracking ◽  
Practical Application ◽  
Adaptive Kalman Filter ◽  
Noise Parameters ◽  
The Stability

AbstractKalman Filter (KF) is the most widely used estimator to estimate and track the states of target. It works well when noise parameters and system models are well defined in advance. Its performance degrades and starts diverging when noise parameters (mainly measurement noise) changes. In the open literature available researchers has used the concept of Fractional Order Kalman Filter (FOKF) to stabilize the KF. However in the practical application there is a variation in the measurement noise, which will leads to divergence and degradation in the FOKF approach. An Innovation Adaptive Estimation (IAE) based FOKF algorithm is presented in this paper. In order to check the stability of the proposed method, Lyapunov theory is used. Position tracking simulation has been performed for performance evaluation, which shows the better result and robustness.

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

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 Modified Sage-Husa Adaptive Kalman Filter-Based SINS/DVL Integrated Navigation System for AUV

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ruixin Liu ◽  
Fucheng Liu ◽  
Chunning Liu ◽  
Pengchao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Positive Definiteness ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
The Stability ◽  
Navigation Accuracy ◽  
Navigation Errors

This paper presents a modified Sage-Husa adaptive Kalman filter-based SINS/DVL integrated navigation system for the autonomous underwater vehicle (AUV), where DVL is employed to correct the navigation errors of SINS that accumulate over time. When negative definite items are large enough, different from the positive definiteness of noise matrices which cannot be guaranteed for the conventional Sage-Husa adaptive Kalman filter, the proposed modified Sage-Husa adaptive Kalman filter deletes the negative definite items of adaptive update laws of the noise matrix to ensure the convergence of the Sage-Husa adaptive Kalman filter. In other words, this method sacrifices some filtering precision to ensure the stability of the filter. The simulation tests are implemented to verify that expected navigation accuracy for AUV can be obtained using the proposed modified Sage-Husa adaptive Kalman filter.

Robust Hybrid Fractional Order Proportional Derivative Sliding Mode Controller for Robot Manipulator Based on Extended Grey Wolf Optimizer

Robotica ◽  
2019 ◽  
Vol 38 (4) ◽  
pp. 605-616 ◽  
Author(s):  
Hossein Komijani ◽  
Mojtaba Masoumnezhad ◽  
Morteza Mohammadi Zanjireh ◽  
Mahdi Mir
Keyword(s):  
Fractional Order ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Grey Wolf Optimizer ◽  
Sliding Mode Controller ◽  
Grey Wolf ◽  
Robust Controller ◽  
The Stability ◽  
Proportional Derivative Controller

SUMMARYThis paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.

scholarly journals A Novel Adaptive Kalman Filter With Colored Measurement Noise

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 74569-74578 ◽  
Author(s):  
Yonggang Zhang ◽  
Guangle Jia ◽  
Ning Li ◽  
Mingming Bai
Keyword(s):  
Kalman Filter ◽  
Measurement Noise ◽  
Adaptive Kalman Filter

scholarly journals Hybrid Adaptive Cubature Kalman Filter with Unknown Variance of Measurement Noise

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4335 ◽  
Author(s):  
Yuepeng Shi ◽  
Xianfeng Tang ◽  
Xiaoliang Feng ◽  
Dingjun Bian ◽  
Xizhao Zhou
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Estimation Method ◽  
Measurement Noise ◽  
Variance Estimators ◽  
Unknown Variance ◽  
Weighted Fusion ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
The Stability

This paper is concerned with the filtering problem caused by the inaccuracy variance of measurement noise in real nonlinear systems. A novel weighted fusion estimation method of multiple different variance estimators is presented to estimate the variance of the measurement noise. On this basis, a hybrid adaptive cubature Kalman filtering structure is proposed. Furthermore, the information filter of the hybrid adaptive cubature Kalman filter is also studied, and the stability and filtering accuracy of the filter are theoretically discussed. The final simulation examples verify the validity and effectiveness of the hybrid adaptive cubature Kalman filtering methods proposed in this paper.

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