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In this paper, the estimation of the parameters of the Two- Wave with Diffuse Power (TWDP) fading distribution from the observation of the received signal envelope is addressed. Specifically, a moment-based approach for the estimation of the parameters K and Δ for this fading model is introduced. We first set the boundaries of the estimation problem by calculating the Cramer-Rao lower Bound...
Estimating parameters of a sum of complex exponentials in white noise is considered in this paper. A simplified maximum likelihood estimation algorithm based on subfactorization of a structured data matrix is proposed, and we show that parameterization of the data model in signal space allows to improve estimation accuracy at low signal-to noise ratio (SNR). Basing on the proposed algorithm the computer...
Professional TV studio footage often poses specific challenges to camera calibration due to lack of features and complex camera operation. As available algorithms often fail, we propose a novel approach based on robust tracking of ellipse and line features of a predefined logo. We further devise a predictive and iterative estimation algorithm, which incorporates confidence measures and filtering....
A cryo Electron Microscopy dataset is composed on tomographic projections of an object (e.g. a macromolecule). The projection orientation information is unknown. The scope of this paper is the projection parameterization in the case of a deformable object. An overview of the parametrization methods is presented. Then a new approach based on manifold learning is detailed. Finally, an evaluation method...
Multipath propagation is a common phenomenon in wireless communication. Knowledge of propagation path parameters such as complex channel gain, propagation delay or angle-of-arrival provides valuable information on the user position and facilitates channel response estimation. A major challenge in channel parameter estimation lies in its multidimensional nature, which leads to large-scale estimation...
We address the problem of sequential parameter estimation over networks using the Bayesian methodology. Each node sequentially acquires independent observations, where all the observations in the network contain signal(s) with unknown parameters. The nodes aim at obtaining accurate estimates of the unknown parameters and to that end, they collaborate with their neighbors. They communicate to the neighbors...
Compressed Sensing (CS) has been successfully applied in a number of imaging systems since it can fundamentally increase frame rates and/or the resolution. In this paper, we apply CS to 3-D surface acquisition using Sheet-of-Light (SOL) scanning. The application of CS could potentially increase the speed of the measurement and/or enhance scan resolution with fewer measurements. To analyze the potential...
Parameters estimation is important for improving the detection performance of detectors. In this paper, we propose a novel estimation method for the parameters of compound-Gaussian distribution with inverse Gaussian texture. We obtain the moments estimation expressions of parameters of compound-Gaussian distribution with inverse Gaussian texture and set the estimation results of moments estimation...
This paper is devoted to the problem of real-time heart rate (HR) response modelling during treadmill exercise. A novel recursive constrained parameter estimation method is developed which in contrast to the conventional parameter estimation schemes (e.g. recursive least squares (RLS) method) can avoid the occurrence of the so-called blowup phenomena. By incorporation of a weighting upon 1) parameter...
As a signal processing method, the least-squares method plays a crucial role in parameter estimation, and great progress has been made in recent decades. However, errors may occur when the parameters to be estimated have some actual physical meaning, e.g., if the human-body temperature is estimated to be 70 °C by a general least-squares method. In this study, we consider solving a particular problem,...
In this paper, a universal control scheme is investigated for a class of nonlinear systems with unknown parameters and nonlinearities like friction, dead-zone, etc. First of all, an adaptive finite-time update law is developed to estimate the unknown parameters by introducing the auxiliary filtered variables for the system states and regressor matrix. Then, a universal controller is proposed based...
Accurate and quick parameter estimation of linear Frequency Modulation (LFM) signal is always the research hot. In this paper, an improved dual-channel joint analysis of measurement algorithm is proposed for the recognition and parameter estimation of cross-channel LFM signal. First, coarse chirp rates are estimated by delay-related correlative dechirp, and then signal is identified coarsely by a...
Exponential demand for broadband applications has elicited renewed interest in characterizing noise in power line communication (PLC) networks for optimal error performance analysis. PLC is economical and ubiquitous, however due to multipath phenomena and nonstationary impulsive noise, the PLC channel is very harsh to signal transmission. Recent studies reveal that PLC noise is cyclostationary and...
The motion dynamics and geometric information are considered to be one of the most useful features for infrared (IR) targets recognition. Especially for the exo-atmospheric target, when a target undergoes micro-motion dynamics in the outer space, such as mechanical vibrations or rotations, it would induce amplitude modulations on signature of target projected area along the Line-of-Sight (LOS) of...
Many research topics related to a battery such as fault detection and estimation have been shown for more than a decade since a battery has became one of the most important components of electrical vehicles (EVs). In this paper, estimation of open circuit voltage and electrical parameters of a battery was investigated. Two separate forgetting factors of Recursive Least Square (RLS) were used to process...
This paper surveys some recent results regarding the Cramér-Rao Lower Bound (CRLB) — the requirements for its existence and its extension to the situation where the parameter's likelihood function (LF) support depends on the parameter to be estimated. In the latter case the conventional CRLB does not hold in general. First we revisit the derivation of the CRLB to elucidate the necessary and sufficient...
For many nonlinear estimation problems, classical lower bounds such as the Cramer-Rao bound (CRB) can characterize the mean squared error (MSE) performance only in the asymptotic region. While more powerful bounds like the Ziv-Zakai bound (ZZB) can also predict the best MSE performance in the nonasymptotic region, they may complicate the computation to an unaffordable extent. In this paper, for estimators...
In this paper, non standard deterministic parameters estimation is considered, i.e. the situation where the probability density function (p.d.f.) parameterized by unknown deterministic parameters results from the marginalization of a joint p.d.f. depending on additional random variables. Unfortunately, in the general case, this marginalization is mathematically intractable, which prevents from using...
An accurate network model is essential for performing detailed analysis of a power system. The quality of many distribution network models is very diverse, especially for low voltage (LV) networks. To help identify areas where the model is incomplete or incorrect, Micro Phasor Measurement Units (μPMUs) can be integrated into a network. These μPMUs would work together, with a trusted cloud back-end...
This paper is concerned with state filtering and the parameter estimation problem of noisy Hodgkin-Huxley neuronal model. The Cubature Kalman filter is applied to solve the joint estimation problem as an effective means of dealing with system noise and observation noise. The proposed state filtering method is based on the only measurable variable - membrane potential. In addition, the method is applicable...
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