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.

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.

scholarly journals Research on sensorless control method of permanent magnet synchronous motor based on iterated cubature Kalman filter

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041989027
Author(s):  
Shi Peicheng ◽  
Wang Chen ◽  
Zhang Rongyun ◽  
Wang Suo
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Method ◽  
Synchronous Motor ◽  
Position Estimation ◽  
Motor Speed ◽  
Rotor Position ◽  
Cubature Kalman Filter

Aiming at the problems of high cost, increased volume, low reliability, and environmental interference caused by sensor installation on permanent magnet synchronous motor, estimation method for motor speed and rotor position is proposed based on iterated cubature Kalman filter algorithm and applied to permanent magnet synchronous motor sensorless control. First, discrete mathematical model of permanent magnet synchronous motor in α-β coordinate system is established. Then, based on cubature Kalman filter and iterated cubature Kalman filter, simulation model of sensorless vector control system with dual closed-loop of permanent magnet synchronous motor speed and current is established. Also, simulation verification of two working conditions with given rotation speed and load is carried out. Finally, hardware experimental verification platform is built based on TMS320F28335 chip. Both simulation analysis and experimental results show that iterated cubature Kalman filter application to sensorless control of permanent magnet synchronous motor demonstrates good anti-load variation interference, stable motor operation, high motor speed and rotor position estimation accuracy, which suits the application with high requirement for precise motor control and mean important reference value and promotion significance.

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.

Velocity and Attitude Matching of Transfer Alignment by Using H∞ Filter

2012 ◽  
Vol 433-440 ◽  
pp. 4861-4864 ◽  
Author(s):  
Li Jun Song ◽  
Deng Feng Cheng ◽  
Wei Lu
Keyword(s):  
Kalman Filter ◽  
White Noise ◽  
Estimation Method ◽  
Measurement Noise ◽  
Engineering Practice ◽  
Transfer Alignment ◽  
Color Noise ◽  
System Noise ◽  
Simulation Results

The H∞ filter is adopted in the transfer alignment (TA) which is realized by the Velocity and Attitude Matching, when the disturbances in measurements are complete unknown. The performance of H∞ filter is compared with kalman filter. The simulation results show both that H∞ filter and kalman filter all are effective and kalman filter is more accurate than H∞ filter when system noise and measurement noise are white noise. But H∞ filter is more accurate than kalman filter when system noise and measurement noise are color noise. H∞ filter is an effective estimation method because H∞ filter is more suitable to engineering practice than kalman filter.

Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Trung Nguyen ◽  
George K. I. Mann ◽  
Andrew Vardy ◽  
Raymond G. Gosine
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
High Speed ◽  
Three Dimensional ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Deterministic Sampling ◽  
Cubature Kalman Filter

This paper presents a computationally efficient sensor-fusion algorithm for visual inertial odometry (VIO). The paper utilizes trifocal tensor geometry (TTG) for visual measurement model and a nonlinear deterministic-sampling-based filter known as cubature Kalman filter (CKF) to handle the system nonlinearity. The TTG-based approach is developed to replace the computationally expensive three-dimensional-feature-point reconstruction in the conventional VIO system. This replacement has simplified the system architecture and reduced the processing time significantly. The CKF is formulated for the VIO problem, which helps to achieve a better estimation accuracy and robust performance than the conventional extended Kalman filter (EKF). This paper also addresses the computationally efficient issue associated with Kalman filtering structure using cubature information filter (CIF), the CKF version on information domain. The CIF execution avoids the inverse computation of the high-dimensional innovation covariance matrix, which in turn further improves the computational efficiency of the VIO system. Several experiments use the publicly available datasets for validation and comparing against many other VIO algorithms available in the recent literature. Overall, this proposed algorithm can be implemented as a fast VIO solution for high-speed autonomous robotic systems.

scholarly journals Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3069 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zheng
Keyword(s):  
State Estimation ◽  
Situation Awareness ◽  
Information Matrix ◽  
Estimation Method ◽  
Measurement Noise ◽  
Distributed State Estimation ◽  
Information Filter ◽  
Fading Factor ◽  
Stability And Accuracy ◽  
The Impact

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.

scholarly journals Randomly Weighted CKF for Multisensor Integrated Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Hua Zong ◽  
Zhaohui Gao ◽  
Wenhui Wei ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Estimation Method ◽  
Experimental Results ◽  
Integrated Systems ◽  
System State ◽  
System Error ◽  
Random Weights ◽  
Cubature Kalman Filter ◽  
Dynamic Modification

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

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