scholarly journals Approaching Target above Ground Tracking Technique Based on Noise Covariance Estimation Method-Kalman Filter

2017 ◽  
Vol 28 (10) ◽  
pp. 810-818
Author(s):  
Young-Sik Park
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Covariance Estimation ◽  
Tracking Technique ◽  
Noise Covariance

Proportional Derivative Controller Using Discrete Kalman Filter Estimation Method for Spacecraft Attitude Control

2015 ◽  
Vol 789-790 ◽  
pp. 923-926 ◽  
Author(s):  
Mohamad Fakhari Mehrjardi ◽  
Hilmi Sanusi ◽  
Mohd Alauddin Mohd Ali ◽  
Montadar Abas Taher
Keyword(s):  
Kalman Filter ◽  
Attitude Control ◽  
Estimation Method ◽  
Angular Acceleration ◽  
Gravity Gradient ◽  
Noise Covariance ◽  
Proportional Derivative Controller ◽  
Typical Satellite ◽  
Noisy Measurements ◽  
Data Updating

This paper deals with the objective of controlling a satellite by driving a six-state discrete Kalman Filter to estimate angular rates of satellite base on control sensor noisy data. A typical satellite is assumed in a special orbit and orbital angular velocity and orbital angular acceleration are established. For completion of simulation linear dynamics model of satellites and environment disturbances model such as solar pressure and gravity gradient torque is derived as well. The simulation is progressed at discrete ten second which assumed as data updating rate from sensor. The noisy measurements are produced by sensor and these data is sent to the discrete Kalman Filter part to estimate the attitude and attitude rate. A right balance for Plant noise covariance matrix is determined and also results show that the rate estimates are appropriate for space missions.

Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance

2014 ◽  
Vol 61 (11) ◽  
pp. 6253-6263 ◽  
Author(s):  
Bo Feng ◽  
Mengyin Fu ◽  
Hongbin Ma ◽  
Yuanqing Xia ◽  
Bo Wang
Keyword(s):  
Kalman Filter ◽  
Covariance Estimation ◽  
Process Noise ◽  
Noise Covariance

scholarly journals Vehicle Driving State Estimation Using an Improved Adaptive Unscented Kalman Filter

2021 ◽  
Author(s):  
Hui Pang ◽  
Peng Wang ◽  
Zijun Xu ◽  
Gang Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Estimation Errors ◽  
Adaptive Unscented Kalman Filter ◽  
Adjustment Strategy ◽  
Noise Covariance ◽  
Adaptive Noise ◽  
Three Degree Of Freedom

Abstract This paper proposes an improved adaptive unscented Kalman filter (iAUKF)-based vehicle driving state estimation method. A three-degree-of-freedom vehicle dynamics model is first established, then the varying principles of estimation errors for vehicle driving states using constant process and measurement noises in the standard unscented Kalman filter (UKF) are compared and analyzed. Next, a new type of normalized innovation square-based adaptive noise covariance adjustment strategy is designed and incorporated into the UKF to derive our expected vehicle driving state estimation method. Finally, a comparative simulation investigation using CarSim and MATLAB/Simulink is conducted to validate the effectiveness of the proposed method, and the results show that our proposed iAUKF-based estimation method has higher accuracy and stronger robustness against the standard UKF algorithm.

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.

scholarly journals A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets

2021 ◽  
Vol 13 (9) ◽  
pp. 1820
Author(s):  
Zhenyu Zhuo ◽  
Yu Zhou ◽  
Lan Du ◽  
Ke Ren ◽  
Yi Li
Keyword(s):  
Kalman Filter ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Motion Feature ◽  
Low Snr ◽  
Noise Covariance ◽  
Modified Kalman Filter ◽  
Noise Robust ◽  
Ransac Algorithm

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods.

scholarly journals Brief communication: An innovation-based estimation method for model error covariance in Kalman filters

2021 ◽  
Author(s):  
Eviatar Bach ◽  
Michael Ghil
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Ensemble Kalman Filter ◽  
Kalman Filters ◽  
Estimation Method ◽  
Model Error ◽  
High Accuracy ◽  
Covariance Estimation ◽  
Observation Error ◽  
Error Covariance

Abstract. We present a simple innovation-based model error covariance estimation method for Kalman filters. The method is based on Berry and Sauer (2013) and the simplification results from assuming known observation error covariance. We carry out experiments with a prescribed model error covariance using a Lorenz (1996) model and ensemble Kalman filter. The prescribed error covariance matrix is recovered with high accuracy.

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