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.

Efficient Approximation of Kalman Filter for Target Tracking

Abstract: A Kalman filter in the Cartesian coordinates is described for a maneuvering target when the radar sensor measures range, bearing, and elevation angles in the polar coordinates at high data rates. An approximate gain computation algorithm is developed to determine the filter gains for on-line microprocessor implementation. In this approach, gains are computed for three uncoupled filters and multiplied by a Jacobian transformation determined from the measured target position and orientation. The algorithm is compared with the extended Kalman filter for a typical target trajectory in a naval gun fire control system. The filter gains and the tracking errors for the proposed algorithm are nearly identical to the extended Kalman filter, while the computation requirements are reduced by a factor of four.

Observer-based state-space current control for a three-phase grid-connected converter equipped with an LCL filter

Abstract: This paper presents a state-space current control method for active damping of the resonance frequency of the LCL filter and setting the dominant dynamics of the converter current through the direct pole placement. A state observer is used, whereupon additional sensors are not needed compared to the conventional L filter design. The relationship between the system delay and instability caused by the resonance phenomenon is considered. Nyquist diagrams are used to examine the parameter sensitivity of the proposed method, and the method is validated with simulations and experiments.

New approach to robust observer design

Abstract: This paper shows that based on the recent development of observer design solution, an observer pole selection method can be formulated to minimize the observer gain to the system input. It is proved that this method is a deterministic approach to the recovery of the loop transfer function and robustness of direct state feedback systems.

Learning an Outlier-Robust Kalman Filter

Abstract: We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers The standard Kalman filter is not robust to outliers, and other variations of the Kalman filter have been proposed to overcome this issue However, these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation procedures Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample A data sample with a smaller weight has a weaker contribution when estimating the current time step's state Using an incremental variational Expectation-Maximization framework, we learn the weights and system dynamics We evaluate our Kalman filter algorithm on data from a robotic dog