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We consider a dynamic game model of power networks with generators and/or consumers, called agents, and one public commission, called the utility; a game with a prescribed dynamic mechanism is performed such that each agent decides a private control to minimize its own cost functional, and the utility manages information transmissions between the utility and agents and decides command signals, called...
Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than two decades. This paper reviews SLAM based on different filtering techniques used to do the state estimation of the mobile robot. The filtering techniques included in this study are Kalman filter, particle filter, H infinity filter. It can be concluded that each filtering technique has...
In this paper, we have investigated the effectiveness of Particle swarm optimization (PSO) with extended Kalman smoother (EKS) for fetal ECG extraction from single channel electrocardiogram (ECG) recorded at abdominal area of mother's skin. The abdominal ECG is considered to be composite as it contains both mother's and fetus ECG and is dominated by maternal ECG component. To extract the fetal ECG,...
In this paper, we consider the problem of tracking a reference trajectory for a simplified car model based on unicycle kinematics, whose position only is measured, and where the control input and the measurements are corrupted by independent Gaussian noises. To tackle this problem we devise a novel observer-controller: the invariant Linear Quadratic Gaussian controller (ILQG). It is based on the Linear...
This paper presents the development of a state observer for a 2-axle railway vehicle with solid axle wheelsets. A plan view model of the vehicle is presented and a Kalman filter is developed to estimate 18 states from 8 inertial measurements. The required measurements are the lateral acceleration and yaw velocity of the vehicle body and the same measurements plus the roll velocity for the two wheelsets,...
If there are significant amounts of data missing, this requires special algorithms for system identification. Such methods have been previoulsy developed and typically result in iterative procedures for the parameter estimation. Since missing data could be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In this case aliasing manifests itself...
In this paper we address the problem of fusing information from biased sensor-data collected by a sensor network. Under the assumption that the biases of the sensors are nuisance parameters, we propose an algorithm that marginalizes them out from the estimation problem. The algorithm uses particle filtering to obtain the unknown states of the system and Kalman filtering for marginalization of the...
The estimation of reaction rates is an important problem in mechanistic modeling, monitoring and control of chemical reactors. In contrast to standard estimation techniques where a model must be chosen for the reaction rates, we consider them in this work as unknown inputs. In this regard, the task is an ill-posed inverse problem, i.e. its solution is not unique and/or unstable with respect to data...
This paper deals with the use of adaptive Kalman filters for fault diagnosis on non linear systems described by multiple models. The matrices Q and R used to build up the gain of Kalman filters are considered here as synthesis parameters for these filters. Adequate variations of these matrices values allow to perform fault detection during the transition phase between two models.
Two approaches are presented in this paper to estimate the state of an induction motor and detect faults: a geometric approach, assuming only that the perturbations belong to known bounded sets with no hypothesis on their distributions inside these sets, and a stochastic approach by Kalman filtering. Recursive and explicit algorithms are presented and illustrated by real data of an induction motor...
This work presents new results for state estimation based on noisy observations suffering from two different types of uncertainties. The first uncertainty is a stochastic process with given statistics. The second uncertainty is only known to be bounded, the exact underlying statistics are unknown. State estimation tasks of this kind typically arise in target localization, navigation, and sensor data...
A method is presented for restoration of noisy bandlimited archived speech records. Speech is modeled with a formant-tracking linear prediction (FTLP) model of the spectral envelope and a harmonic noise model (HNM) of the excitation. The time-varying trajectories of the parameters of the LP and HNM models are tracked with Viterbi classifiers and denoised with Kalman filters. A frequency domain pitch...
An algorithm for discrimination and detection of the two phenomena double talk and abrupt changes in the echo path is proposed for fading channels. Being able to discriminate and detect these two phenomena is crucial since the echo canceler must react differently. The suggested detection scheme is based on a sequential detection approach. The communication channel is modeled as a randomly time-varying...
The Frisch scheme, that considers additive independent noises on the measures of the input and output of a process, has recently led to the development of specific identification procedures. The obtained models, that have already been used to implement smoothing procedures congruent with the scheme, are here used to develop a filtering algorithm.
The optimal control strategy for discrete time multiple model is described. Simulation of control and on-line estimation of model probability is shown. Robustness of stability and comparison of the classical LQ control and LQ control based on multiple models is presented.
Old gramophone recordings are corrupted with a wideband noise (granulation noise) and impulsive disturbances (cliks, pops, record scratches) — both caused by aging and/or mishandling of the vinyl material. The paper presents an improved method of gramophone noise reduction which makes use of two signals obtained when a mono record is played back using the stereo equipment.
This paper studies a Quantized Gossip-based Interactive Kalman Filtering (QGIKF) algorithm implemented in a wireless sensor network, where the sensors exchange their quantized states with neighbors via inter-sensor communications. We show that with the information loss due to quantization, the network can still achieve weak consensus, i.e., the estimation error variance sequence at a randomly selected...
Ultra high definition television (UHDTV) has gradually entered our daily life. However, because of the large data of UHDTV, it is hard to render images in real time. We proposed an eye tracking based solution using the concept of uncrowned window from vision research. The theory of uncrowded window suggests that human vision can only effectively recognize objects inside a small window. Object features...
Particle filtering has been widely accepted as an important methodology for processing data represented by state-space models characterized by nonlinearities and/or non-Gaussianities. It is also well documented that particle filtering deteriorates quickly in performance when the dimension of the tracked state becomes large. This limits its application in many science/engineering problems. Previously...
This paper investigates the problem of how to improve angle-of-arrival (AOA) target localization accuracy by finding an optimal AOA sensor deployment strategy in 3D space. Under the assumption of constant absolute elevation angles for the sensors, a novel and simple optimal sensor deployment criterion is proposed based on minimizing the trace of inverse Fisher information matrix. Our analysis shows...
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