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.

Generalization of exponentially weighted RLS algorithm based on a state-space model

Abstract: We develop a generalized RLS (G-RLS) algorithm described by a state-space model through some modification of the procedure for Kalman filter derivation. It is shown that the G-RLS algorithm reduces to the conventional RLS when the state transition matrix is an identity matrix, and that the G-RLS algorithm without exponential weighting and Kalman filtering become identical when the state model is an unforced dynamical model. The G-RLS algorithm does not require model statistics, and can be implemented once the forgetting factor is chosen. The performances of the G-RLS and Kalman filtering are compared through computer simulation. Specifically, they are applied to the derivation of variable loop gains of a digital phase-locked loop (DPLL). The results indicate that the G-RLS algorithm can act like the Kalman filter if its forgetting factor is properly chosen.

Extended robust Kalman filter for attitude estimation

Abstract: In this study, the authors deal with inertial measurement units subject to uncertainties. They propose an extended robust Kalman filter (ERKF) in a predictor–corrector form to estimate a rigid body attitude. The filter is developed based on regularisation and penalisation whose approaches present the advantage of encompassing in a unified framework all state and output uncertain parameters of the system. The ERKF is tuned based on two degree of freedom which belong to a certain interval known a-priori, useful for online applications. The attitude estimation system proposed takes into account a rigid body model formulated in terms of quaternions. Experimental results are presented based on a comparative study among the ERKF, the standard extended Kalman filter and an 𝓗∞ filter.

Experimental Study of Iterated Kalman Filters for Simultaneous Localization and Mapping of Autonomous Mobile Robots

Abstract: In this paper, we investigate the role of iteration in Kalman filters family for improvement of the estimation accuracy of states in simultaneous localization and mapping (SLAM). The linearized error propagation existing in Kalman filters family can result in large errors and inconsistency in the SLAM problem. One approach to alleviate this situation is the use of iteration in extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) based SLAM. The main contribution is to present that the iterated versions of Kalman filters can increase consistency and robustness of these filters against linear error propagation. Experimental results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear observation model in EKF-SLAM and SPKF-SLAM algorithms.

Kalman filter based equalization for digital multicarrier communications systems

Abstract: A computer-implemented approach for reducing distortion in digital communications systems generally involves obtaining an optimal estimation of the original data using a Kalman filter with an increased length state to remove substantially all ISI in the time domain. The use of an increased length state in the Kalman filter provides a more accurate estimate of the transmitted data with relatively little additional computational cost. Subsequent equalization may also be performed in the frequency domain to correct any residual amplitude and phase distortion.