Deterministic sampling-based switching kalman filtering for vehicle tracking

2006 ◽  
Author(s):  
H. Veeraraghavan ◽  
N. Papanikolopoulos ◽  
P. Schrater
Keyword(s):  
Kalman Filtering ◽  
Vehicle Tracking ◽  
Deterministic Sampling

scholarly journals Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

2020 ◽  
Author(s):  
Tai-shan Lou ◽  
Dong-xuan Han ◽  
Xiao-liang Yang ◽  
Su-xia Jiang
Keyword(s):  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Sampling Methods ◽  
Estimation Error ◽  
Sensitivity Function ◽  
Estimation Accuracy ◽  
Uncertain Parameters ◽  
Excellent Performance ◽  
Deterministic Sampling

To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

scholarly journals Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

2020 ◽  
Author(s):  
Tai-shan Lou ◽  
Dong-xuan Han ◽  
Xiao-liang Yang ◽  
Su-xia Jiang
Keyword(s):  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Sampling Methods ◽  
Estimation Error ◽  
Sensitivity Function ◽  
Estimation Accuracy ◽  
Uncertain Parameters ◽  
Excellent Performance ◽  
Deterministic Sampling

To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

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.

Application of the Three State Kalman Filtering for Moving Vehicle Tracking

2016 ◽  
Vol 14 (5) ◽  
pp. 2072-2076 ◽  
Author(s):  
Roberto Olivera ◽  
Reynel Olivera ◽  
Osbaldo Vite ◽  
Hamurabi Gamboa ◽  
Miguel Angel Navarrete ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Vehicle Tracking ◽  
Moving Vehicle

Delivering Sustainable Water Systems Using Kalman Filter and Statistical Approach

2017 ◽  
Vol 4 (1) ◽  
pp. 41-52
Author(s):  
Dedy Loebis
Keyword(s):  
Kalman Filtering ◽  
Statistical Approach ◽  
Water Systems ◽  
Data Sets ◽  
Pressure Data ◽  
Burst Event ◽  
Sample Data ◽  
Other Information ◽  
Supply Systems ◽  
Frequency Features

This paper presents the results of work undertaken to develop and test contrasting data analysis approaches for the detection of bursts/leaks and other anomalies within wate r supply systems at district meter area (DMA)level. This was conducted for Yorkshire Water (YW) sample data sets from the Harrogate and Dales (H&D), Yorkshire, United Kingdom water supply network as part of Project NEPTUNE EP/E003192/1 ). A data analysissystem based on Kalman filtering and statistical approach has been developed. The system has been applied to the analysis of flow and pressure data. The system was proved for one dataset case and have shown the ability to detect anomalies in flow and pres sure patterns, by correlating with other information. It will be shown that the Kalman/statistical approach is a promising approach at detecting subtle changes and higher frequency features, it has the potential to identify precursor features and smaller l eaks and hence could be useful for monitoring the development of leaks, prior to a large volume burst event.

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