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.

A Comparitive Study Of Kalman Filter, Extended Kalman Filter And Unscented Kalman Filter For Harmonic Analysis Of The Non-Stationary Signals

Abstract: The accurate measurement of harmonic level is essential for designing harmonic filters and monitoring the stress to which the communication devices are subjected due to harmonics and specifying digital filtering techniques for phasor measurements . This paper presents an integrated approach to design an optimal estimator for the measurement of frequency and harmonic components of a time varying signal embedded in low signal-to noise ratio. This led to the study of Kalman, Extended Kalman and Unscented Kalman filter characteristics and a subsequent implementation of the study to design these filters. We have employed the Extended Kalman filter and Unscented Kalman filter algorithms to estimate the voltage magnitude in the presence of random noise and distortions. Kalman filter being an optimal estimator to track the signal corrupted with noise and harmonic distortion quite accurately. Tracking of harmonic components of a dynamic signal in communication system can easily be done using EKF and UKF algorithms and their results are compared.

Comparing a Kalman Filter and a Particle Filter in a Multiple Objects Tracking Application

Abstract: Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A particle filter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman filter with an association algorithm for each of the objects to track. Both algorithms are described in the paper and the results obtained with their real-time execution in the mentioned application are shown. Finally interesting conclusions extracted from this comparison are remarked at the end.

Tracking of 2D or 3D Irregular Movement by a Family of Unscented Kalman Filters

Abstract: This paper reports on the design of an object tracker that utilizes a family of unscented Kalman filters, one for each tracked object. This is a more efficient design than having one unscented Kalman filter for the family of all moving objects. The performance of the designed and implemented filter is demonstrated by using simulated movements, and also for object movements in 2D and 3D space.

Improved Kalman filter initialisation using neurofuzzy estimation

Abstract: It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs, but unfortunately their performance is extremely sensitive to state initialisation accuracy: good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter divergence. Divergence is generally due to excessive observation noise and leads to error magnitudes that quickly become unbounded (R.J. Fitzgerald, 1971). When a filter diverges, it must be re initialised but because the observations are extremely poor, re initialised states will have poor estimates. The paper proposes that if neurofuzzy estimators produce more accurate state estimates than those calculated from the observed noisy inputs (using the known state model), then neurofuzzy estimates can be used to initialise the states of Kalman and extended Kalman filters. Filters whose states have been initialised with neurofuzzy estimates should give improved performance by way of faster convergence when the filter is initialised, and when a filter is re started after divergence.

Robust Filtering for State and Fault Estimation of Linear Stochastic Systems with Unknown Disturbance

Abstract: This paper presents a new robust filter structure to solve the simultaneous state and fault estimation problem of linear stochastic discrete-time systems with unknown disturbance. The method is based on the assumption that the fault and the unknown disturbance affect both the system state and the output, and no prior knowledge about their dynamical evolution is available. By making use of an optimal three-stage Kalman filtering method, an augmented fault and unknown disturbance models, an augmented robust three-stage Kalman filter (ARThSKF) is developed. The unbiasedness conditions and minimum-variance property of the proposed filter are provided. An illustrative example is given to apply this filter and to compare it with the existing literature results.