In this paper we describe a new nonlinear estimator for filtering systems with nonlinear process and observation models, based on the optimization with RGO (Restricted Genetic Optimization). Simulation results are used to compare the performance of this method with EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), SNF (Second-order Nonlinear Filter), SIF (Single-stage Iterat...

This paper is an attempt to generalize the results obtained earlier and presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of parti...

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...

The GPS/INS integration system estimates the navigation errors as well as internal sensor errors through the use of a Kalman filter. The tight integration processing mode uses the GPS range and range-rate measurements in a nonlinear Kalman filter. Extended Kalman filtering (EKF) has been discussed in many publications dealing with GPS/INS integration. The recently developed sigma-point Kalman f...

The realization of a digital phase-locked loop (DPLL) requires to choose a suitable phase detector and to design an appropriate loop filter; these tasks are commonly nontrivial in most applications. In this paper, the phase detector is examined, and a simple model is given to describe the characteristics of the timing function. The DPLL system is then formulated as a state estimation problem; t...

The Kalman filter has been widely used for estimation and tracking of linear systems since its formulation in 1960 due to its simplicity and robustness. In many chemical engineering applications the extended Kalman filter (EKF) is often used to deal with certain classes of nonlinear systems. In spite of that, designing an EKF for highly nonlinear processes is not a trivial task, particularly th...

We introduce the antiparticle filter, AF, a new type of recursive Bayesian estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. We show that for a classic problem of robot localization the AF can substantially outperform these other filters in some situations. The AF estimates the posterior distribution as an auxiliary variable...

For parameter estimations, we have developed a extended kalman filter(EKF) and unscented kalman filter(UKF) for linear as well as non-linear spacecraft systems. We have described the differences of two approaches mathematical equation for modeling of the system value of mean square error, error covariance and For state estimation of satellite, two different type of filters has been described i....

This paper presents the performance of the extended Kalman filter (EKF) for speed sensorless indirect field oriented control (IFOC) of a squirrel cage induction motor (IM) supplied by a pulse width modulated (PWM) voltage-source inverter. The rotor speed of the induction motor is estimated, according to this method, using the direct measurements of the stator voltages and currents without the s...

This paper describes an enhancement method for location tracking system based on Received Signal Strength Indicator (RSSI)values and Extended Kalman Filter (EKF). The enhancement is based on pre calibration of measurement vectors and individual position block usage in final estimation of the target's position. The EKF and the described enhancement methods are used on a standard Wireless Local A...