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.

Computational requirements for a discrete Kalman filter

Abstract: How practical is a Kalman filter? One answer to this question is provided by the computational requirements for the filter. Computational requirements-computational time per cycle (iteration) and required storage-determine minimum sampling rates and computer memory size. These requirements are provided in this paper as functions of the dimensions of the important system matrices for a discrete Kalman filter. Two types of measurement processing are discussed: simultaneous and sequential. It is shown that it is often better to process statistically independent measurements in more than one batch and then to use sequential processing than to process them together via simultaneous processing.

Optimal Linear Filters for Discrete-Time Systems With Randomly Delayed and Lost Measurements With/Without Time Stamps

Abstract: A novel model is developed to describe possible random delays and losses of measurements transmitted from a sensor to a filter by a group of Bernoulli distributed random variables. Based on the new developed model, an optimal linear filter dependent on the probabilities is presented in the linear minimum variance sense by the innovation analysis approach when packets are not time-stamped. The solution to the optimal linear filter is given in terms of a Riccati difference equation and a Lyapunov difference equation. A sufficient condition for the existence of the steady-state filter is given. At last, the optimal filter is given by Kalman filter when packets are time-stamped.

Minimal order state observers for bilinear systems

Abstract: This paper considers a minimal order state observer for a bilinear system. The given observer is an extension of one for a linear system and can be designed without considering inputs because the estimation error is made to be independent of inputs. The necessary condition for the existence of a minimal order observer and a standard form of a bilinear system satisfying this condition are presented. A standard form of a minimal order state observer is also obtained and the design procedure of this observer is shown. As an example, the observer of a d.c. motor is designed.

A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements

Abstract: A new globally convergent nonlinear observer called the modified gain extended Kalman observer (MGEKO) is developed for a special class of systems. The stochastic stability of this observer used as a filter (now called the MGEKF), is analyzed in the probabilistic Hilbert space L2. Sufficient conditions for the MGEKF to be asymptotically stable are established. Finally, the MGEKO and the MGEKF are applied to the three-dimensional bearing only measurement problem (BOMP) where the EKF often shows erratic behaviors.

Comparative study of a sliding-mode observer and Kalman filters for full state estimation in an induction machine

Abstract: The paper compares a sliding-mode observer with the standard and extended versions of the Kalman filter for full state estimation in an induction machine. The design method for the sliding-mode observer is presented and shown to possess invariant dynamic modes, which can be assigned independently to achieve the desired performance. The chattering concerned with the sliding-mode method can be eliminated by modifying the observer gain, without sacrificing robustness and precision. The indices of comparison are dynamic performance, robustness against parameter uncertainties, noise sensitivity, stability and computational complexity. Simulation and experimental results are presented in this paper. Important conclusions, together with recommendations for observer selection, regarding the effectiveness of the various schemes are given.