MVDR ALGORITHM BASED LINEAR ANTENNA ARRAY PERFORMANCE ASSESSMENT FOR ADAPTIVE BEAMFORMING APPLICATION

The performance of Minimum Variance Distortionless Response (MVDR) beamformer is sensitive to errors such as the steering vector errors, the finite snapshots, and unsatisfactory null-forming level. In this paper, a combination of MVDR with linear antenna arrays (LAAs) for two scanning angles process in the azimuth and elevation are used to illustrate the MVDR performance against error which results in acquiring the desired signal and suppressing the interference and noise. The impact of various parameters, such as the number of elements in the array, space separation between array elements, the number of interference sources, noise power level, and the number of snapshots on the MVDR are investigated. The MVDR performance is evaluated with two important metrics: beampattern of two scanning angles and Signal to Interference plus Noise Ratio (SINR). The results found that the MVDR performance improves as the number of array elements increases. The beampattern relies on the number of elements and the separation between array elements. The best interelement spacing obtained is 0.5λ that avoids grating lobes and mutual coupling effects. Besides, the SINR strongly depends on the noise power label and a number of snapshots. When the noise power label increased, the MVDR performance degraded as well the null width increases in the elevation direction as well as more accurate resolution occurred when the number of snapshots increased. Finally, it is found the proposed method achieves SINR better than existing techniques

A weighted MVDR beamformer based on SVM learning for sound source localization

3noA weighted minimum variance distortionless response (WMVDR) algorithm for near-field sound localization in a reverberant environment is presented. The steered response power computation of the WMVDR is based on a machine learning component which improves the incoherent frequency fusion of the narrowband power maps. A support vector machine (SVM) classifier is adopted to select the components of the fusion. The skewness measure of the narrowband power map marginal distribution is showed to be an effective feature for the supervised learning of the power map selection. Experiments with both simulated and real data demonstrate the improvement of the WMVDR beamformer localization accuracy with respect to other state-of-the-art techniques.partially_openopenSalvati, Daniele; Drioli, Carlo; Foresti, Gian LucaSalvati, Daniele; Drioli, Carlo; Foresti, Gian Luc

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

Incoherent Frequency Fusion for Broadband Steered Response Power Algorithms in Noisy Environments

The steered response power (SRP) algorithms have been shown to be among the most effective and robust ones in noisy environments for direction of arrival (DOA) estimation. In broadband signal applications, the SRP methods typically perform their computations in the frequency-domain by applying a fast Fourier transform (FFT) on a signal portion, calculating the response power on each frequency bin, and subsequently fusing these estimates to obtain the final result. We introduce a frequency response incoherent fusion method based on a normalized arithmetic mean (NAM). Experiments are presented that rely on the SRP algorithms for the localization of motor vehicles in a noisy outdoor environment, focusing our discussion on performance differences with respect to different signal-to-noise ratios (SNR), and on spatial resolution issues for closely spaced sources. We demonstrate that the proposed fusion method provides higher resolution for the delay-and-sum SRP, and improved performances for minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC

Skaalattu harva lineaarinen regressio elastisella verkolla

Scaled linear regression is a form of penalized linear regression in which the penalty level is automatically scaled in proportion to the estimated noise level in the data. This makes the penalty parameter independent of the noise scale enabling an analytical approach for choosing an optimal penalty level for a given problem. In this thesis, we first review conventional penalized regression methods, such as ridge regression, lasso, and the elastic net. Then, we review some scaled sparse linear regression methods, the most relevant of which is the scaled lasso, also known as square-root lasso. As an original contribution, we propose two elastic net formulations, which extend the scaled lasso to the elastic net framework. We demonstrate by numerical examples that the proposed estimators improve upon the scaled lasso in the presence of high correlations in the feature space. As a real-world application example, we apply the proposed estimators in a simulated single snapshot direction-of-arrival (DOA) estimation problem, where we show that the proposed estimators perform better, especially when the angles of incidence of the DOAs are oblique with respect to the uniform linear array (ULA) axis.Skaalattu lineaarinen regressio käsittää regularisointimenetelmiä, joissa regularisointitermin painoa skaalataan datasta estimoidun kohinatason perusteella. Tämä poistaa optimaalisen regularisointitermin riippuvuuden tuntemattomasta kohinatasosta, mikä mahdollistaa analyyttisesti johdettujen regularisointitermien käytön. Diplomityössä tarkasteltiin ridge, lasso ja elastinen verkko -regressiomenetelmien ominaisuuksia sekä skaalattuja regressiomenetelmiä, kuten skaalattua lasso- eli neliöjuurilassomenetelmää. Diplomityössä kehitettiin täysin uudet estimaattorit: skaalattu elastinen verkko ja neliöjuuri elastinen verkko, jotka toimivat paremmin kuin skaalattu lasso multikollineaarisissa tilanteissa, mikä osoitettiin numeerisilla simulaatioilla. Esimerkkinä käytännön sovelluksesta, uusia estimaattoreita sovellettiin DOA-estimoinnissa, jossa pyritään antenniryhmän avulla määrittämään signaalin tulosuunta. Saatujen tulosten perusteella voitiin päätellä, että diplomityössä ehdotetut estimaattorit pystyivät määrittämään tulosuunnan paremmin kuin skaalattu lasso etenkin, kun signaalin tulokulma oli suuri antenniryhmän akselin suhteen

Three-Dimensional Geometry Inference of Convex and Non-Convex Rooms using Spatial Room Impulse Responses

This thesis presents research focused on the problem of geometry inference for both convex- and non-convex-shaped rooms, through the analysis of spatial room impulse responses. Current geometry inference methods are only applicable to convex-shaped rooms, requiring between 6--78 discretely spaced measurement positions, and are only accurate under certain conditions, such as a first-order reflection for each boundary being identifiable across all, or some subset of, these measurements. This thesis proposes that by using compact microphone arrays capable of capturing spatiotemporal information, boundary locations, and hence room shape for both convex and non-convex cases, can be inferred, using only a sufficient number of measurement positions to ensure each boundary has a first-order reflection attributable to, and identifiable in, at least one measurement. To support this, three research areas are explored. Firstly, the accuracy of direction-of-arrival estimation for reflections in binaural room impulse responses is explored, using a state-of-the-art methodology based on binaural model fronted neural networks. This establishes whether a two-microphone array can produce accurate enough direction-of-arrival estimates for geometry inference. Secondly, a spherical microphone array based spatiotemporal decomposition workflow for analysing reflections in room impulse responses is explored. This establishes that simultaneously arriving reflections can be individually detected, relaxing constraints on measurement positions. Finally, a geometry inference method applicable to both convex and more complex non-convex shaped rooms is proposed. Therefore, this research expands the possible scenarios in which geometry inference can be successfully applied at a level of accuracy comparable to existing work, through the use of commonly used compact microphone arrays. Based on these results, future improvements to this approach are presented and discussed in detail

Development and Improvement of Airborne Remote Sensing Radar Platforms

With the recent record ice melt in the Arctic as well as the dramatic changes occurring in the Antarctic, the need and urgency to characterize ice sheets in these regions has become a research thrust of both the NSF and NASA. Airborne remote sensing is the most effective way to collect the necessary data on a large scale with fine resolution. Current models for determining the relationship between the world's great ice sheets and global sea-level are limited by the availability of data on bed topography, glacier volume, internal layers, and basal conditions. This need could be satisfied by equipping long range aircraft with an appropriately sensitive suite of sensors. The goal of this work is to enable two new airborne radar installations for use in cryospheric surveying, and improve these systems as well as future systems by addressing aircraft integration effects on antenna-array performance. An aerodynamic fairing is developed to enable a NASA DC-8 to support a 5-element array for CReSIS's MCoRDS radar, and several structures are also developed to enable a NASA P-3 to support a 15-element MCoRDS array, as well as three other radar antenna-arrays used for cryospheric surveying. Together, these aircraft have flown almost 200 missions and collected 550 TB of unique science data. In addition, a compensation method is developed to improve beamforming and clutter suppression on wing-mounted arrays by mitigating phase center errors due to wing-flexure. This compensation method is applied to the MVDR beamforming algorithm to improve clutter suppression by using element displacement information to apply appropriate phase shifts. The compensation demonstrated an average SINR increase of 5-10 dB. The hardware contributions of this work have substantially contributed to the state-of-the-art for polar remotes sensing, as evidenced by new data sets made available to the science community and widespread use and citation of the data. The investigations of aircraft integration effects on antenna-arrays will improve future data sets by characterizing the performance degradation. The wing-flexure compensation will greatly improve beam formation and clutter suppression. Increased clutter suppression in airborne radars is crucial to improving next generation ice sheet models and sea-level rise predictions