Object Identification and Tracking via Noise Updated Iterative Extended Kalman Filter

Volume 1 ◽  
2004 ◽  
Author(s):  
A. Bondarenko ◽  
Y. Halevi ◽  
M. Shpitalni
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Mathematical Formulation ◽  
Substantial Improvement ◽  
Object Identification ◽  
State Variables ◽  
Unknown Parameters ◽  
Method Of Solution ◽  
Simultaneous Identification

The paper considers the problem of simultaneous identification and trajectory tracking of moving objects (either 2D or 3D) from moving sensors. The identification is parametric and is based on knowing the family that the object belongs to, e.g. ball, ellipsoid, box, etc. The mathematical formulation results in implicit measurements, i.e. an algebraic equation that includes both state variables and actual measurements. The method of solution is via Extended Kalman Filter where the unknown parameters are regarded as additional state variables. Standard Extended Kalman Filter and Iterative Extended Kalman Filter yielded unsatisfactory results, mainly due to the nonlinearity of the measurements in both the state vector and the noise. A new algorithm, called Noise Updated Iterative Extended Kalman Filter is suggested. Its deviation from the standard iterative Kalman filter is in estimating the measurement noise at each iteration. The estimated noise is then used in the linearization stage to obtain a more accurate linear approximation. The method has been applied to the online identification and tracking problem, with substantial improvement in performance.

scholarly journals SPEED SENSORLESS CONTROL FOR PMSM DRIVES USING EXTENDED KALMAN FILTER

2021 ◽  
Vol 84 (1) ◽  
pp. 77-83
Author(s):  
Mohamad Ikhwan Nordin ◽  
Jurifa Mat Lazi ◽  
Md Hairul Nizam Talib ◽  
Zulkifilie Ibrahim
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Algorithm ◽  
Initial Position ◽  
Accurate Estimation ◽  
State Variables ◽  
Speed Sensorless ◽  
Non Linear System

In this paper, Sensorless Permanent Magnet Synchronous Motor (PMSM) using Extended Kalman Filter (EKF) is presented. The previous PMSM drive uses a sensor to measure the motor’s speed. Then the idea is to replace the sensor by using sensorless drives based on the observer. For the conventional observer, it’s only good for low current and low-speed applications. Moreover, it is hard to detect the phase voltage due to the non-existence of neutral wire. Therefore, this project proposes sensorless control using an EKF. This method provides an optional estimation algorithm for the non-linear system that can produce a fast and accurate estimation of state variables. The accurate estimation will reduce the noise and ripple of the system. Additionally, the EKF do not require the information of mechanical parameters and the initial position of the rotor, making the construction is easy and simple. In this paper, the fundamental of the EKF algorithm is explained and the simulation results for different speeds and loads are presented. The noise reduction test is also conducted to measure the flux current with and without the filter. The simulation study is achieved using MATLAB/Simulink to verify the effectiveness of the proposed method. The results of the simulation show that the sensorless PMSM drives using EKF have lower overshoot and faster rise time during start-up conditions and have lower undershoot during the loaded condition. It also can be concluded that the proposed sensorless PMSM drive using EKF has good speed control accuracy and can reduce the current noise.

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm

2017 ◽  
pp. 76-99 ◽  
Author(s):  
Kamalanand Krishnamurthy
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Models ◽  
Three Dimensional ◽  
Model Parameters ◽  
State Variables ◽  
Three Dimensional Model ◽  
Biomedical Systems ◽  
Estimation Of Model Parameters

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

Design of Extended Kalman Filter for a Shape Memory Alloy Manipulator

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
William T. Baumann
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
Noisy Measurements

This paper presents an Extended Kalman Filter (EKF) for estimation of the state variables of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA). A state space model for the SMA manipulator is presented. The model includes nonlinear dynamics of the manipulator, constitutive model of Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. In the experimental setup, angular position of the arm is the only state variable that is measured. The other state variables of the system are arm’s angular velocity, SMA wire’s stress, temperature and the Martensite factor, which are not available experimentally due to measurement difficulties. Hence, a model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter. This estimator predicts the state vector at each time step and corrects its prediction based on the angular position of the arm which can be measured experimentally. The state variables collected through model simulations are also used to evaluate the performance of the EKF. Several EKF simulations are presented that show accurate, and robust performance of the estimator for different types of inputs.

Estimating Parameters and Refining Thermal Models by Using the Extended Kalman Filter Approach

2004 ◽  
Vol 126 (5) ◽  
pp. 809-817 ◽  
Author(s):  
Ashley F. Emery
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Deterministic Model ◽  
Dimensional System ◽  
Time Behavior ◽  
State Variables ◽  
Physical Processes ◽  
One Dimensional ◽  
Full Knowledge ◽  
Filter Approach

Parameter estimation is based upon a comparison of predicted deterministic model responses to data. The models are often numerical, e.g., finite volume, with intrinsic inaccuracies. In addition, the models typically assume a full knowledge of the physical processes. By using the concept of state variables and employing the extended Kalman filter approach it is possible to include additional effects in the model to achieve better agreement between the model and the data. This paper describes such an approach to the estimation of thermal conductivity in a transiently heated and cooled one-dimensional system and shows that it leads to a resolution of questions about the time behavior of the residuals previously observed in an estimation based upon the least squares analysis.

Application of the Extended Kalman Filter to Control of a Shape Memory Alloy Arm

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
Daniel J. Inman
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Vector ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
State Variable

This paper presents a robust nonlinear control that uses a state variable estimator for control of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA) wire. A model for SMA actuated manipulator is presented. The model includes nonlinear dynamics of the manipulator, a constitutive model of the Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. The current experimental setup allows for the measurement of only one state variable which is the angular position of the arm. Due to measurement difficulties, the other three state variables, arm angular velocity and SMA wire stress and temperature, cannot be directly measured. A model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter (EKF). This estimator predicts the state vector at each time step and corrects its prediction based on the angular position measurements. The estimator is then used in a nonlinear and robust control algorithm based on Variable Structure Control (VSC). The VSC algorithm is a control gain switching technique based on the arm angular position (and velocity) feedback and EKF estimated SMA wire stress and temperature. The state vector estimates help reduce or avoid the undesirable and inefficient overshoot problem in SMA one-way actuation control.

scholarly journals The Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems

Enfoque UTE ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 120-130
Author(s):  
Holger Ignacio Cevallos Ulloa ◽  
Gabriel Intriago ◽  
Douglas Plaza ◽  
Roger Idrovo
Keyword(s):  
Kalman Filter ◽  
Power Systems ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Electrical Power ◽  
The State ◽  
Dynamic State ◽  
State Variables ◽  
Electrical Power Systems ◽  
Error Index

The state estimation and the analysis of load flow are very important subjects in the analysis and management of Electrical Power Systems (EPS). This article describes the state estimation in EPS using the Extended Kalman Filter (EKF) and the method of Holt to linearize the process model and then calculates a performance error index as indicators of its accuracy. Besides, this error index can be used as a reference for further comparison between methodologies for state estimation in EPS such as the Unscented Kalman Filter, the Ensemble Kalman Filter, Monte Carlo methods, and others. Results of error indices obtained in the simulation process agree with the order of magnitude expected and the behavior of the filter is appropriate due to follows adequately  the true value of the state variables. The simulation was done using Matlab and the electrical system used corresponds to the IEEE 14 and 30 bus test case systems. State Variables to consider in this study are the voltage and angle magnitudes.

scholarly journals Tracking Objects on Detector Response using Extended Kalman Filter

2020 ◽  
Vol 9 (4) ◽  
pp. 1614-1620
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Video Frame ◽  
Detector Response ◽  
Data Set ◽  
Detection And Tracking ◽  
Cumulative Summation ◽  
Tracking Objects

Video analytics plays a very important role in identification or detection and tracking of objects, this intern find application in many fields and domains. Novel learning methods or techniques built on Neural Networks requires larger dataset for training the results, the output obtained depends on how well the training is done. The proposed method of Weighted Cumulative Summation (WCS) is an approach based on background modelling to segment the moving objects. This method adapts and tunes the background variations instantaneously as the video frame arrives. The segmentation obtained is compared with other basic methods. The result obtained infers improvements in segmentation and in removal of ghost effect in the video. Extended Kalman Filter (EKF) is used to track the detector response. The responses of the detection from WCS are provided as input to EKF to track the moving object. The results are tabulated and represented in the form of graphs for analysis. The results are compared with three different video datasets and the results are noticeably good. The methods WCS can be used in the applications were data set is not available.

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