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.

Estimation of a dispersion parameter in discrete Kalman filtering

Abstract: In the application of the discrete Kalman filter, it occasionally happens that one of the noise covariance matrices is known except for a scalar multiplier. Algorithms are derived to estimate such a parameter using the covariance-matching technique.

State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

Abstract: A battery’s state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less than 2%.

Extended Braking Stiffness Estimation Based on a Switched Observer, With an Application to Wheel-Acceleration Control

Abstract: In the context of hybrid anti-lock brake systems (ABS), a closed-loop wheel-acceleration controller based on the observation of the extended braking stiffness (XBS) is provided. Its objective is to improve the system's robustness with respect to changes in the environment (as changes in road conditions, brake properties, etc.). The observer design is based on Burckhardt's tyre model, which provides a wheel acceleration dynamics that is linear up to time-scaling. The XBS is one of the state variables of this model. The paper's main result is an observer that estimates this unmeasured variable. When the road conditions are known, a three-dimensional observer solves the problem. But, for unknown road conditions, a more complex four-dimensional observer must be used instead. In both cases, the observer's convergence is analysed using tools for switched linear systems that ensure uniform exponential stability (provided that a dwell-time condition is satisfied). Both experiments and simulations confirm the convergence properties predicted by the theoretical analysis.

Comparison of Four Filtering Options for a Radar Tracking Problem

Abstract: : Four different filtering options are considered for the problem of tracking an exoatmospheric ballistic target with no maneuvers The four filters are an alpha-beta filter, an augmented alpha-beta filter, a decoupled Kalman filter, and a fully-coupled extended Kalman filter These filters are listed in the order of increasing computational complexity All of the filters can track the target with some degree of accuracy While the pure alpha-beta filter appreciably lags the other filters in performance for this problem, its augmented version is very competitive with the extended Kalman filter under benign conditions Perhaps the most surprising result is that under all conditions examined, the decoupled (linear) Kalman filter, which is at least an order of magnitude less computationally complex, performs nearly identical to the coupled, extended Kalman filter Four different filtering options are considered for the problem of tracking an exoatmospheric ballistic target with no maneuvers The four filters are an alpha-beta filter, an augmented alpha-beta filter, a decoupled Kalman filter, and a fully-coupled extended Kalman filter These filters are listed in the order of increasing computational complexity All of the filters can track the target with some degree of accuracy While the pure alpha-beta filter appreciably lags the other filters in performance for this problem, its augmented version is very competitive with the extended Kalman filter under benign conditions Perhaps the most surprising result is that under all conditions examined, the decoupled (linear) Kalman filter, which is at least an order of magnitude less computationally complex, performs nearly identical to the coupled, extended Kalman filter

Comparison of Kalman Filter and Wavelet Filter for Denoising

Abstract: This paper presents denoising the signal using wavelet filter and Kalman filter. The noise is zero mean and the variance value is 0.001. Kalman filter removes disturbances or faults from the signal by using initialization and propagation of error covariance statistics. Implementation of Kalman filter is impractical in large scale models as shown for the oscillator system. As an alternative wavelet filter has been used for the same system. Coiflet 2 which is orthogonal wavelet has been used. Soft thresholding has been applied. Decomposition is performed at level 9. The results of wavelet filter and Kalman filter are shown. Response of wavelet filter is better when compared with Kalman filter result