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.

Finite time convergent observers for nonlinear systems

Abstract: In this note we consider the state estimation problem for nonlinear systems. In a first step we outline the design of an observer for single output nonlinear systems in observer normal form. The estimate of this observer converges to the exact system state within predefined finite time. This observer is then used in the design of a finite time convergent observer for multiple output systems that are given in block triangular observer normal form. Specifically, the block triangular structure is exploited by recursively designing observers that take the state estimates obtained by the observers for the "previous" blocks into account. As shown the estimation error of the resulting overall observer converges to zero within finite time. The outlined approach is exemplified considering the state estimation of a eights order two link elastic robot.

Phase and amplitude imaging from noisy images by Kalman filtering.

Abstract: We propose and demonstrate a computational method for complex-field imaging from many noisy intensity images with varying defocus, using an extended complex Kalman filter. The technique offers dynamic smoothing of noisy measurements and is recursive rather than iterative, so is suitable for adaptive measurements. The Kalman filter provides near-optimal results in very low-light situations and may be adapted to propagation through turbulent, scattering, or nonlinear media.

Non-linear observer for slip estimation of skid-steering vehicles

Abstract: Accurate estimation of slip is essential in developing autonomous navigation strategies for mobile vehicles operating in unstructured terrain. In this paper, a sliding mode observer is firstly constructed to estimate slip parameters based on the kinematics model of a skid-steering vehicle and trajectory measurement. The stability of the sliding mode observer is given in a mathematical context. Slip estimation schemes using an extended Kalman filter and direct mathematical inversion of the kinematic equations are also presented for comparison purposes. It is shown that the non-linear sliding mode observer is more accurate than the other two methods. The robustness and superior performance of the sliding mode observer is demonstrated using both simulation and experimental results. A camera based system is used to measure the vehicle trajectory during experimental validation

Kalman Filtering Applied to Statistical Forecasting

Abstract: This paper describes the use of the Kalman Filter in a certain ciass of forecasting problems. The time series is assumed to be modeled as a time varying mean with additive noise. The mean of the time series is assumed to be a linear combination of known functions. The coefficients appearing in the linear combination are unknown. Under such assumptions, the time series can be described as a linear system with the state vector of the system being the unknown parameters and present value of the mean of the process. The Kalman Filter can be used under these circumstances to obtain an “optimal” estimate of the state vector. One of the distinct advantages of the Kalman Filter is that time varying coefficients can be permitted in the model. Examples using the Kalman Filter in forecasting are presented.