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...

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...

We investigate the problem of tracking a moving source in shallow ocean in a Bayesian framework, using acoustic field measurements which are more informative than the commonly employed bearings-only or time-delay measurements. The acoustic field measurement model is described and compared with the bearings-only measurement model. A general approach to Bayesian filtering based on the Gaussian ap...

An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems called Smart Sampling Kalman Filter (S2KF) is introduced. It is based on a new low-discrepancy Dirac Mixture approximation of Gaussian densities. The approximation comprises an arbitrary number of optimally and deterministically placed samples in the entire state space, so that the filter resolution can be adapted to eithe...

This work proposes a novel application of existing powerful nonlinear filters, such as the standard Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented by a large number of parameters when parameterized. We couple these filters with the po...