This brief technical note elaborates three well-known state estimators, which are used extensively in practice. These are the rather old-fashioned extended Kalman filter (EKF) and the recently-designed cubature Kalman filtering (CKF) and un-scented Kalman filtering (UKF) algorithms. Nowadays, it is commonly accepted that the contemporary techniques outperform always the traditional EKF in the a...

This paper presents a novel and cost effective method to be used in the optimization of the Gaussian Frequency Shift Keying (GFSK) at the receiver of the Bluetooth communication system. The proposed method enhances the performance of the noncoherent demodulation schemes by improving the Bit Error Rate (BER) and Frame Error Rate (FER) outcomes. Linear, Extended, and Unscented Kalman Filters are ...

In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF. This algorit...

This paper proposes a composite local path planning method for multi-robot formation navigation with path deviation prevention using a repulsive function, A-star algorithm, and unscented Kalman filter (UKF). The repulsive function in the potential field method is used to avoid collisions among robots and obstacles. The A-star algorithm helps the robots to find an optimal local path. In addition...

The navigation problem associated with terminal aircraft guidance refers to position determination of an individual vehicle with respect to some point local to the environment as is the case with aircraft landing systems. This paper presents results for a relative navigation filter that achieves CAT3-level precision from a customized navigation satellite receiver's data message and the NORAD SD...

In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways: (1) as a building block for approximating the l...

An unscented Kalman filter (UKF) for matrix Lie groups is proposed where the time propagation of state formulated on algebra. This done with kinematic differential equation logarithm, inverse right Jacobian used. The sigma points can then be expressed as logarithms in vector form, and computation mean covariance resulting formulation to a large extent based form is, therefore, closer UKF system...

In this paper, phasor measurement unit (PMU) data-based synchronous generator model identification is carried out using unscented Kalman filter (UKF). The identification not only gives the model of a synchronous generator’s swing dynamics, but also gives its turbine-governor model along with the primary and secondary frequency control block models. PMU measurements of active power and voltage m...

Based on the Unscented Kalman Filter (UKF), the nonlinear filter is presented for parameter estimation in linear system with correlated noise where the unknown parameters are estimated as a part of an enlarged state vector. To avoid the computational burden in determining the state estimates when only the parameter estimates are required, a new form of UKF, where the state consists only of the ...

This paper presents the novel Rao-Blackwellised particle filter (RBPF) for mobile robot simultaneous localization and mapping (SLAM) using monocular vision. The particle filter is combined with unscented Kalman filter (UKF) to extending the path posterior by sampling new poses that integrate the current observation which drastically reduces the uncertainty about the robot pose. The landmark pos...