Despite developments in sensor technology, monitoring a biological process using regular sensor measurements is often difficult. Development of Bayesian state observers, such as extended Kalman filter(EKF), is an attractive alternative for soft-sensing of such complex systems. The performance of EKF is dependent on the accurate characterisation of the uncertainties in the state dynamics and in ...

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...

Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based l...

The extended Kalman filter (EKF) is a popular state estimation method for nonlinear dynamical models. The model error covariance matrix is often seen as a tuning parameter in EKF, which is often simply postulated by the user. In this paper, we study the filter likelihood technique for estimating the parameters of the model error covariance matrix. The approach is based on computing the likeliho...

The main goal of this research is to identify the system parameters of the dynamics of Rotor/Active Magnetic Bearing (Rotor/AMB) system employed in turbo molecular pumps (TMPs). The identification approach adopted in this work is based on experimental analysis and the application of Fuzzy Logic Adaptive ControlExtended Kalman Filter (FLAC-EKF). The estimation error for either system state or sy...

This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V/Hz controller and a field-oriented co...

Most UAV(unmaned aerial vehicle) systems use GPS signals only to locate the emitter’s position. However GPS signals contain unwanted information contaminated by environmental components and many interference signals. In this paper, to obtain TDoA signal, we use two UAVs which are equipped with embedded wireless sensors. Under the real geolocation circumstance, it is very difficult to estimate t...

This paper presents a sensing method to measure three-dimensional (3-D) information in an underwater environment using an acoustic camera. Acoustic cameras can acquire clear information even in turbid water which is difficult to photograph with an optical camera. In addition, its detection range is extensive. Compared to traditional sensors, acoustic cameras with no restrictions on vision are t...

A new Gaussian mixture filter has been developed, one that uses a re-sampling step in order to limit the covariances of its individual Gaussian components. The new filter has been designed to produce accurate solutions of difficult nonlinear/nonBayesian estimation problems. It uses static multiple-model filter calculations and Extended Kalman Filter (EKF) approximations for each Gaussian mixand...

This paper presents HybridSLAM: an approach to SLAM which combines the strengths and avoids the weaknesses of two popular mapping strategies: FastSLAM and EKF-SLAM. FastSLAM is used as a front-end, producing local maps which are periodically fused into an EKF-SLAM back-end. The use of FastSLAM locally avoids linearisation of the vehicle model and provides a high level of robustness to clutter a...