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.

Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter

Abstract: This study represents a method for estimating the state of charge (SOC) of lithium-ion batteries using radial basis function (RBF) networks and square-root unscented Kalman filter (KF). The RBF network is trained offline by sampled data from the battery in the charging process. This type of neural network finds the non-linear relation which is required in the state-space equations. The state variables include the battery terminal voltage and the SOC, at the previous sample and the present sample, respectively. The proposed method is tested experimentally on a lithium-ion battery with 1.2 Ah capacity to estimate the actual SOC of the battery. The experimental results of the proposed method show some advantages, which include: (i) it is not very sensitive to determine, precisely, the measurement and process noise covariance matrices such as Kalman filter and (ii). It contains lower noise on the output, in comparison with Adaptive extended Kalman filter (EKF).

Recursive filtering algorithm for a two-dimensional system

Abstract: An approximate two-dimensional recursive filtering algorithm that parallels the one-dimensional Kalman filter is presented for a causal system considered by Habibi [1]. A numerical result is also shown.

Theoretical analysis and tuning criteria of the Kalman filter-based tracking loop

Abstract: In recent years, Kalman filter (KF)-based tracking loop architectures have gained much attention in the Global Navigation Satellite System field and have been widely investigated due to its robust and better performance compared with traditional architectures. However, less attention has been paid to the in-depth theoretical analysis of the tracking structure and to the effects of Kalman tuning. A new approach is proposed to analyze the KF-based tracking loop. A control system model is derived according to the mathematical expression of the Kalman system. Based on this model, the influence of the choice of the setting parameters on the temporal evolution of the system response is discussed from the perspective of a control system. As a result, a reasoned and complete suite of criteria to tune the initial error covariance as well as the process and measurements noise covariances is demonstrated. Furthermore, a strategy is presented to make the system more robust in higher order dynamics without degrading the accuracy of carrier phase and Doppler frequency estimates.

Robustness and Sensitivity Metrics for Tuning the Extended Kalman Filter

Abstract: In this paper, a robustness metric and a sensitivity metric have been defined, which can be used to determine a suitable combination of the filter tuning parameters of the extended Kalman filter. These metrics are related to the innovation covariance and their derivation necessitates a change of paradigm from the estimated states to the estimated measurements. The characteristics of these metrics have been inferred in detail and these have been used to predict the root-mean-squared error (RMSE) performances in a 2-D falling body problem. To do so, a general method has been proposed in this paper to obtain an initial choice of the filter tuning parameters based on the available literature. The RMSE performances are then obtained for a range of variation of the most critical tuning parameter, namely the filter process noise covariance. In general, the characteristics predicted from the metrics correlate significantly with the RMSE performances, and hence these can be used to obtain the desired tradeoff between robustness and sensitivity in various filter applications.