Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.

Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems

Abstract: Recently, the range of available Radio Frequency Identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.

A comparative study of rotor flux estimation in induction motors with a nonlinear observer and the extended Kalman filter

Abstract: The paper presents a comparison of performances and characteristics of a nonlinear observer and observation algorithms based on the Kalman filter for induction motor rotor flux estimation, inserted in a field oriented control scheme. The construction of the considered algorithms is described in detail, and the different design issues are explained. For fair comparison the same measurements are assumed available to both deterministic and stochastic estimators, and the same controller parameters are used in simulation. The performances of the estimators are compared either in terms of observation errors during transient and steady state operations either in terms of computational complexity in on-line implementation. >

An observer design for uniformly observable non-linear systems

Abstract: In this paper, we give a new observer design methodology for single-output uniformly observable systems. In fact, the proposed observer is an improvement of the high gain observer given by Gauthier et al. (1992). The improvement lies basically in the fact that part of the non-linearity of the system is incorporated in the observer design strategy. Consequently, unlike the observer given in Gauthier et al. (1992), the gain of the improved observer is function of the state estimate. A comparison of the proposed observer to that given by Gauthier et al. (1992), is made via a simulation example dealing with a flexible joint mechanism and it is shown that the improved observer is more robust with regard to measurement noise and present less transient oscillations. An extension of the design strategy is also presented for a more general class of non-linear systems.

Second-Order-Optimal Minimum-Energy Filters on Lie Groups

Abstract: Systems on Lie groups naturally appear as models for physical systems with full symmetry. We consider the state estimation problem for such systems where both input and output measurements are corrupted by unknown disturbances. We provide an explicit formula for the second-order-optimal nonlinear filter on a general Lie group where optimality is with respect to a deterministic cost measuring the cumulative energy in the unknown system disturbances (minimum-energy filtering). The resulting filter depends on the choice of affine connection which encodes the nonlinear geometry of the state space. As an example, we look at attitude estimation, where we are given a second order mechanical system on the tangent bundle of the special orthogonal group SO(3), namely the rigid body kinematics together with the Euler equation. When we choose the symmetric Cartan-Schouten (0)-connection, the resulting filter has the familiar form of a gradient observer combined with a perturbed matrix Riccati differential equation that updates the filter gain. This example demonstrates how to construct a matrix representation of the abstract general filter formula.

Predict chaotic time-series using unscented Kalman filter

Abstract: Although the extended Kalman filter is a widely used estimator for nonlinear systems, it has two drawbacks: linearization can produce unstable filters and it is hard to implement the derivation of the Jacobian matrices. This work presents a new method of predicting Mackey-Glass equation based on unscented Kalman filter. The principle of unscented transform is analyzed and the algorithm of UKF is discussed And then EKF and UKF methods are used to estimate the noisy chaotic time-series, and the estimation errors between two different algorithms are compared. Simulation results show this filter can predict chaotic time-series more effectively and accurately than extended Kalman filter.