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.

Cubature information filter and its applications

Abstract: This paper presents a new estimation algorithm called cubature information filtering for nonlinear systems. The proposed algorithm is developed from an extended information filter and a recently developed cubature Kalman filter. Unlike the extended Kalman filter, the proposed filter does not require the evaluation of Jacobians during state estimation. The efficacy of the proposed algorithm is demonstrated by simulation examples on frequency demodulation and localization problem and is compared with unscented information filtering.

Noise robust voice activity detection based on switching Kalman filter

Abstract: This paper addresses the problem of voice activity detection (VAD) in noisy environments. The VAD method proposed in this paper is based on a statistical model approach, and estimates statistical models sequentially without a priori knowledge of noise. Namely, the proposed method constructs a clean speech / silence state transition model beforehand, and sequentially adapts the model to the noisy environment by using a switching Kalman filter when a signal is observed. In this paper, we carried out two evaluations. In the first, we observed that the proposed method significantly outperforms conventional methods as regards voice activity detection accuracy in simulated noise environments. Second, we evaluated the proposed method on a VAD evaluation framework, CENSREC-1-C. The evaluation results revealed that the proposed method significantly outperforms the baseline results of CENSREC-1-C as regards VAD accuracy in real environments. In addition, we confirmed that the proposed method helps to improve the accuracy of concatenated speech recognition in real environments.

Nonlinear Estimation With State-Dependent Gaussian Observation Noise

Abstract: We consider the problem of estimating the state of a system when measurement noise is a function of the system's state. We propose generalizations of the extended Kalman filter and the iterated extended Kalman filter that can be utilized when the state estimate distribution is approximately Gaussian. The state estimate is computed by an iterative root-searching method that maximizes a maximum likelihood function. The new filter allows for the consistent treatment of a class of control problem involving nonlinear estimation from measurements with state-dependent noise. The effectiveness of the estimation algorithm is illustrated for a control problem with a mobile bearing-only sensor.

Power system harmonic analysis using the Kalman filter

Abstract: This paper explores the practical application of the Kalman filter to the analysis of harmonic levels in power systems. The merits and limitations of different possible implementations are investigated and the effect of fundamental frequency variation is examined. The tuning of the Kalman filter for desired dynamic response is discussed and an adaptive tuning algorithm derived for the improved convergence of nonlinear models. The effectiveness of the resulting schemes are tested under a variety of typical power system operating conditions.

Nonlinear state observation using H/sub /spl infin//-filtering Riccati design

Abstract: The authors propose an observer for continuous-time nonlinear systems. The observer gain is computed by a Riccati differential equation similar to the extended Kalman filter. They prove that under certain conditions the proposed observer is an exponential observer by choosing an appropriate Lyapunov function. Furthermore, the authors explore some important relations of the proposed observer to robust control theory and H/sub /spl infin//-filtering. To examine the practical usefulness of the proposed observer they applied it to an induction motor for the estimation of the rotor flux and the angular velocity.