Detection of Wideband Signal Number Based on Bootstrap Resampling

Knowing source number correctly is the precondition for most spatial spectrum estimation methods; however, many snapshots are needed when we determine number of wideband signals. Therefore, a new method based on Bootstrap resampling is proposed in this paper. First, signals are divided into some nonoverlapping subbands; apply coherent signal methods (CSM) to focus them on the single frequency. Then, fuse the eigenvalues with the corresponding eigenvectors of the focused covariance matrix. Subsequently, use Bootstrap to construct the new resampling matrix. Finally, the number of wideband signals can be calculated with obtained vector sequences according to clustering technique. The method has a high probability of success under low signal to noise ratio (SNR) and small number of snapshots

Signal Separation Using a Mathematical Model of Physiological Signals for the Measurement of Heart Pulse Wave Propagation With Array Radar

The arterial pulse wave, which propagates along the artery, is an important indicator of various cardiovascular diseases. By measuring the displacement at multiple parts of the human body, pulse wave velocity can be estimated from the pulse transit time. This paper proposes a technique for signal separation using an antenna array, so that pulse wave propagation can be measured in a non-contact manner. The body displacements due to the pulse wave at different body parts are highly correlated, and cannot be accurately separated using techniques that assume independent or uncorrelated signals. The proposed method formulates the signal separation as an optimization problem, based on a mathematical model of the arterial pulse wave. The objective function in the optimization comprises four terms that are derived based on a small-displacement approximation, unimodal impulse response approximation, and a causality condition. The optimization process was implemented using a genetic algorithm. The effectiveness of the proposed method is demonstrated through numerical simulations and experiments.Comment: This paper has been published in IEEE Access (Early Access), 12 pages, 17 figure

Vector sensors for underwater : acoustic communications

Acoustic vector sensors measure acoustic pressure and directional components separately. A claimed advantage of vector sensors over pressure-only arrays is the directional information in a collocated device, making it an attractive option for size-restricted applications. The employment of vector sensors as a receiver for underwater communications is relatively new, where the inherent directionality, usually related to particle velocity, is used for signal-to-noise gain and intersymbol interference mitigation. The fundamental question is how to use vector sensor directional components to bene t communications, which this work seeks to answer and to which it contributes by performing: analysis of acoustic pressure and particle velocity components; comparison of vector sensor receiver structures exploring beamforming and diversity; quanti cation of adapted receiver structures in distinct acoustic scenarios and using di erent types of vector sensors. Analytic expressions are shown for pressure and particle velocity channels, revealing extreme cases of correlation between vector sensors' components. Based on the correlation hypothesis, receiver structures are tested with simulated and experimental data. In a rst approach, called vector sensor passive time-reversal, we take advantage of the channel diversity provided by the inherent directivity of vector sensors' components. In a second approach named vector sensor beam steering, pressure and particle velocity components are combined, resulting in a steered beam for a speci c direction. At last, a joint beam steering and passive time-reversal is proposed, adapted for vector sensors. Tested with two distinct experimental datasets, where vector sensors are either positioned on the bottom or tied to a vessel, a broad performance comparison shows the potential of each receiver structure. Analysis of results suggests that the beam steering structure is preferable for shorter source-receiver ranges, whereas the passive time-reversal is preferable for longer ranges. Results show that the joint beam steering and passive time-reversal is the best option to reduce communication error with robustness along the range.Sensores vetoriais acústicos (em inglês, acoustic vector sensors) são dispositivos que medem, alem da pressão acústica, a velocidade de partícula. Esta ultima, é uma medida que se refere a um eixo, portando, esta associada a uma direção. Ao combinar pressão acústica com componentes de velocidade de partícula pode-se estimar a direção de uma fonte sonora utilizando apenas um sensor vetorial. Na realidade, \um" sensor vetorial é composto de um sensor de pressão (hidrofone) e um ou mais sensores que medem componentes da velocidade de partícula. Como podemos notar, o aspecto inovador está na medição da velocidade de partícula, dado que os hidrofones já são conhecidos.(...)This PhD thesis was supported by the Brazilian Navy Postgraduate Study Abroad Program Port. 227/MB-14/08/2019

Direction of Arrival Estimation for Radio Positioning: a Hardware Implementation Perspective

Nowadays multiple antenna wireless systems have gained considerable attention due to their capability to increase performance. Advances in theory have introduced several new schemes that rely on multiple antennas and aim to increase data rate, diversity gain, or to provide multiuser capabilities, beamforming and direction finding (DF) features. In this respect, it has been shown that a multiple antenna receiver can be potentially used to perform radio localization by using the direction of arrival (DoA) estimation technique. In this field, the literature is extensive and gathers the results of almost four decades of research activities. Among the most cited techniques that have been developed, we find the so called high-resolution algorithms, such as multiple signal classification (MUSIC), or estimation of signal parameters via rotational invariance (ESPRIT). Theoretical analysis as well as simulation results have demonstrated their excellent performance to the point that they are usually considered as reference for the comparison with other algorithms. However, such a performance is not necessarily obtained in a real system due to the presence of non idealities. These can be divided into two categories: the impairments due to the antenna array, and the impairments due to the multiple radio frequency (RF) and acquisition front-ends (FEs). The former are strongly influenced by the manufacturing accuracy and, depending on the required DoA resolution, have to be taken into account. Several works address these issues in the literature. The multiple FE non idealities, instead, are usually not considered in the DoA estimation literature, even if they can have a detrimental effect on the performance. This has motivated the research work in this thesis that addresses the problem of DoA estimation from a practical implementation perspective, emphasizing the impact of the hardware impairments on the final performance. This work is substantiated by measurements done on a state-of-the-art hardware platform that have pointed out the presence of non idealities such as DC offsets, phase noise (PN), carrier frequency offsets (CFOs), and phase offsets (POs) among receivers. Particularly, the hardware platform will be herein described and examined to understand what non idealities can affect the DoA estimation performance. This analysis will bring to identify which features a DF system should have to reach certain performance. Another important issue is the number of antenna elements. In fact, it is usually limited by practical considerations, such as size, costs, and also complexity. However, the most cited DoA estimation algorithms need a high number of antenna elements, and this does not yield them suitable to be implemented in a real system. Motivated by this consideration, the final part of this work will describe a novel DoA estimation algorithm that can be used when multipath propagation occurs. This algorithm does not need a high number of antenna elements to be implemented, and it shows good performance despite its low implementation/computational complexity

Adaptive Signal Processing Techniques and Realistic Propagation Modeling for Multiantenna Vital Sign Estimation

Tämän työn keskeisimpänä tavoitteena on ihmisen elintoimintojen tarkkailu ja estimointi käyttäen radiotaajuisia mittauksia ja adaptiivisia signaalinkäsittelymenetelmiä monen vastaanottimen kantoaaltotutkalla. Työssä esitellään erilaisia adaptiivisia menetelmiä, joiden avulla hengityksen ja sydämen värähtelyn aiheuttamaa micro-Doppler vaihemodulaatiota sisältävät eri vastaanottimien signaalit voidaan yhdistää. Työssä johdetaan lisäksi realistinen malli radiosignaalien etenemiselle ja heijastushäviöille, jota käytettiin moniantennitutkan simuloinnissa esiteltyjen menetelmien vertailemiseksi. Saatujen tulosten perusteella voidaan osoittaa, että adaptiiviset menetelmät parantavat langattoman elintoimintojen estimoinnin luotettavuutta, ja mahdollistavat monitoroinnin myös pienillä signaali-kohinasuhteen arvoilla.This thesis addresses the problem of vital sign estimation through the use of adaptive signal enhancement techniques with multiantenna continuous wave radar. The use of different adaptive processing techniques is proposed in a novel approach to combine signals from multiple receivers carrying the information of the cardiopulmonary micro-Doppler effect caused by breathing and heartbeat. The results are based on extensive simulations using a realistic signal propagation model derived in the thesis. It is shown that these techniques provide a significant increase in vital sign rate estimation accuracy, and enable monitoring at lower SNR conditions

Phaseless computational imaging with a radiating metasurface

Computational imaging modalities support a simplification of the active architectures required in an imaging system and these approaches have been validated across the electromagnetic spectrum. Recent implementations have utilized pseudo-orthogonal radiation patterns to illuminate an object of interest---notably, frequency-diverse metasurfaces have been exploited as fast and low-cost alternative to conventional coherent imaging systems. However, accurately measuring the complex-valued signals in the frequency domain can be burdensome, particularly for sub-centimeter wavelengths. Here, computational imaging is studied under the relaxed constraint of intensity-only measurements. A novel 3D imaging system is conceived based on 'phaseless' and compressed measurements, with benefits from recent advances in the field of phase retrieval. In this paper, the methodology associated with this novel principle is described, studied, and experimentally demonstrated in the microwave range. A comparison of the estimated images from both complex valued and phaseless measurements are presented, verifying the fidelity of phaseless computational imaging.Comment: 18 pages, 18 figures, articl

Underdetermined Blind Source Separation in Echoic Environments Using DESPRIT

The DUET blind source separation algorithm can demix an arbitrary number of speech signals using M=2 anechoic mixtures of the signals. DUET however is limited in that it relies upon source signals which are mixed in an anechoic environment and which are sufficiently sparse such that it is assumed that only one source is active at a given time frequency point. The DUET-ESPRIT (DESPRIT) blind source separation algorithm extends DUET to situations where M≥2 sparsely echoic mixtures of an arbitrary number of sources overlap in time frequency. This paper outlines the development of the DESPRIT method and demonstrates its properties through various experiments conducted on synthetic and real world mixtures