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This work proposes distributed recursive least squares (RLS) strategies for adaptive reconstruction and learning of signals defined over graphs. First, we introduce a centralized RLS estimation strategy with probabilistic sampling, and we propose a sparse sensing method that selects the sampling probability at each node in the graph in order to guarantee adaptive signal reconstruction and a target...
Hand gestures are tools for conveying information, expressing emotion, interacting with electronic devices or even serving disabled people as a second language. A gesture can be recognized by capturing the movement of the hand, in real time, and classifying the collected data. Several commercial products such as Microsoft Kinect, Leap Motion Sensor, Synertial Gloves and HTC Vive have been released...
In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.
The Euclid satellite aims to measure accurately the global properties of the Universe, with particular emphasis on the properties of the mysterious Dark Energy that is driving the acceleration of its expansion. One of its two main observational probes relies on accurate measurements of the radial distances of galaxies through the identification of important features in their individual light spectra...
The problem of localizing an IR-UWB transmitter from the signals received at several anchors is considered. The positioning problem is typically solved in a two-step approach where in the first step the Time of Arrival (TOA) is estimated independently at each anchor, and the position estimate is found in a second step. However, this approach can be improved, especially in challenging scenarios, if...
In this paper, the focus is on optimal sensor placement and power rating selection for parameter estimation in wireless sensor networks (WSNs). We take into account the amount of energy harvested by the sensing nodes, communication link quality, and the observation accuracy at the sensor level. In particular, the aim is to reconstruct the estimation parameter with minimum error at a fusion center...
In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters...
Hyperparameter estimation is a recurrent problem in the signal and statistics literature. Popular strategies are cross-validation or Bayesian inference, yet it remains an active topic of research in order to offer better or faster algorithms. The models considered here are sparse regression models with convex or non-convex group-Lasso-like penalties. Following the recent work of Pereyra et al. [1]...
In this paper we analyze the use of tight frames for the problem of localizing a source from noisy time-difference of arrival measurements. Based on the Fisher information matrix, we show that positioning the sensor network according to a tight frame that also obeys some internal symmetries provides the best average localization accuracy. We connect our result to previous approaches from the literature...
The limited availability and the lack of continuity in the service of Global Positioning Satellite Systems (GNSS) in harsh environments is a critical issue for Intelligent Transport Systems (ITS) applications relying on the position. This work is developed within the framework of vehicle-to-everything (V2X) communication, with the aim to guarantee a continuous position availability to all the agents...
An information-theoretic approach is described to estimate the determinant of the covariance matrix of a random vector sequence (a common task in a wide range of estimation and detection problems in signal processing for communications). The method is based on a prior entropy-based processing of the data using kernels and offers robustness against small-entropy contamination. The trade-off between...
In this paper, a new synthetic frequency estimator of sinusoid signal based on discrete Fourier transform (DFT) amplitudes is proposed. Compared with the existing methods, the proposed method has the advantages of low computational complexity and high precision. The method has two main stages. Firstly, N-point sampled data in the time domain DFT is performed and the coarse estimation is completed...
In this paper, we propose the bias-compensated distributed diffusion LMS algorithm to estimate the parameter in the presence of multiplicative regressor noise to mitigate the estimation bias caused by multiplicative regressor noise. In the case where the multiplicative regressor noise exists, the estimate produced by standard distributed diffusion LMS algorithm will be biased. To validate the algorithm,...
Compressive sensing (CS) is a viable source of an innovative 5G system, what's more, it's an effective technology to deal with the data redundancy problem of massive machine-to-machine communication (MMC), since it enables the recovery of sparse and approximately sparse signals with significantly fewer samples than demanded by Nyquist-Shannon sampling theory. Interference in signal will lead a series...
We focus on the 3-dimensional (3D) source localization passively by using TDOA and AOA in the presence of sensor errors. Determining the position from the TDOA and AOA measurements is not an easy task because the relationship between them is nonlinear. We present a new WLS solution that the TDOA equation is simpler than relevant literature (Yin and Wan, A Simple and Accurate TDOA-AOA Localization...
The layout of the anchors is one of the major factors that affect the positioning accuracy of timing-based localization systems. Existing anchor layout optimization typically resorts to the use of the Cramer-Rao lower bound (CRLB), which takes all the anchors into consideration to obtain the globally optimal layout (for some target locations only). Except for some special cases, the CRLB based globally...
In the last edition of the IEC Standard 61000-4-30 a Rapid Voltage Change (RVC) is defined as “a quick transition in RMS voltage between two steady-state conditions, during which the voltage does not exceed the dip/swell thresholds [1].” Because of their intrinsically sudden nature, RVC-like events are generally hard to detect and to monitor. In fact, no clear criteria to set the detection thresholds...
This work examines a cross-layered caching problem for distributed estimation in wireless sensor networks (WSNs). In WSNs, large amounts of data are produced continuously over time, and storing all the data collected from the sensors can be costly. In distributed estimation applications, sensors first gather information about a common phenomenon, and then forward the information to a fusion center...
In this paper, we shall develop a generic channel estimation framework based on the convex formulation for dense cloud radio access networks (Cloud-RAN). Due to the training resource constraint and the large number of transmit antennas, the pilot length is smaller than the antenna number, and thus channel estimation becomes an ill-posed inverse problem. By observing that the wireless channel possesses...
In this paper, we propose a new channel tracking method for massive multiple-input multiple-output (MIMO) systems under both the time-varying and spatial-varying circumstance. With spatial-temporal basis expansion model (ST-BEM), the channel information is decomposed into the spatial information and gain information, where the former is determined by the central angle as well as the angular spread...
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