Extended Kalman filter (EKF) has been widely used for attitude determination in various satellite missions. However, it 1 requires an extensive computational power which is not suitable for nano-satellite application. This paper proposes a gain-scheduled 2 EKF (GSEKF) to reduce the computational requirement in nano-satellite attitude determination process. The proposed GSEKF 3 eliminates the on...

The standard formulations of the Kalman filter (KF) and extended Kalman filter (EKF) require the storage and multiplication of matrices of size n × n, where n is the size of the state space, and the inversion of matrices of size m × m, where m is the size of the observation space. Thus when both m and n are large, implementation issues arise. In this paper, we advocate the use of the limited me...

The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is propose...

This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative.

: The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type fil...

In the present paper, we consider the problem of parameter estimation of wideband polynomial phase signals (PPS) impinging on a uniform linear array antenna. The parameters of interest are the polynomial phase coefficients and the direction of arrival of the signal. The principle of estimation is based on the introduction of an exact but unfortunately nonlinear state space modelization, of the ...

The accurate state of charge (SOC) estimation of the LiFePO4 battery packs used in robot applications is required for better battery life cycle, performance, reliability, and economic issues. In this paper, a new SOC estimation method, “Modified ECE + EKF”, is proposed. The method is the combination of the modified Equivalent Coulombic Efficiency (ECE) method and the Extended Kalman Filter (EKF...

In this paper, a novel method based on a combination of Extended Kalman Filter (EKF) with Self-adaptive Differential Evolution (SaDE) algorithm to estimate rotor position, speed and machine states for a Permanent Magnet Synchronous Motor (PMSM) is proposed. In the proposed method, as a first step SaDE algorithm is used to tune the noise covariance matrices of state noise and measurement noise i...

Localizing a vehicle consists in estimating its state by merging data from proprioceptive sensors (inertial measurement unit, gyrometer, odometer, etc.) and exteroceptive sensors (GPS sensor). A well known solution in state estimation is provided by the Kalman filter. But, due to the presence of nonlinearities, the Kalman estimator is applicable only through some alternatives among which the Ex...

The extended Kalman filter (EKF) is considered one of the most effective methods for both nonlinear state estimation and parameter estimation (e.g., learning the weights of a neural network). Recently, a number of derivative free alternatives to the EKF for state estimation have been proposed. These include the Unscented Kalman Filter (UKF) [1, 2], the Central Difference Filter (CDF) [3] and th...