DOCSLIB.ORG

Spatial Array Processing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Spatial Array Processing Spatial Array Processing Signal and Image Processing Seminar Murat Torlak Telecommunications & Information Sys. Eng. The University of Texas at Austin 1 Introduction A sensor array is a group of sensors located at spatially separated points Sensor array processing focuses on data collected at the sensors to carry out a given estimation task Application Areas – Radar – Sonar – Seismic exploration – Anti-jamming communications – YES! Wireless communications 2 Problem Statement s1 (t) s (t) 2 θ 1 θ 2 ∆ x (t) x (t) x (t) x (t) x1 (t) 2 3 x4 (t) 5 6 Find 1. Number of sources 2. Their direction-of-arrivals (DOAs) 3. Signal Waveforms 3 Assumptions Isotropic and nondispersive medium – Uniform propagation in all directions Far-Field – Radius of propogation >> size of array – Plane wave propogation Zero mean white noise and signal, uncorrelated No coupling and perfect calibration 4 Antenna Array Source θ X X X X12X 3 45∆ Array Response Vector–Far-Field Assumption Narrowband - Delay = Phase Shift Assumption j2f 4 sin =c j2f 44 sin =c T c c a = [1;e ;::: ;e ] = xt Single Source Case 2 3 3 3 2 2 x t s t 1 1 1 6 7 7 7 6 6 j 2f c 6 7 7 7 6 6 x t s t e 2 1 6 7 7 7 6 6 6 7 7 7 6 6 s t = a s t = 6 7 7 7 6 6 1 1 . 1 6 7 7 7 6 . 6 . 6 7 7 7 6 6 4 5 5 5 4 . 4 . j 2f M 1 c x t s t M 1 e 1 M = 4 sin =c where 1 . 5 General Model d By superposition, for signals, xt = a s t+ + a s t 1 1 d d d X = a s t k k k=1 Noise d X xt = a s t+ nt k k k =1 = ASt+ nt where A = [a ;::: ;a ] 1 d and T St = [s t;::: ;s t] : 1 d 6 Low-Resolution Approach:Beamforming Basic Idea d d X X i1j 2f 4 sin =c jw i1 c k k x t = = e s t = s te i k k k =1 k =1 w = 2 4 sin =c i = 1;::: ;M k where k and . fw g Use DFT (or FFT) to find the frequencies k 2 3 1 1 1 6 7 jw jw jw 6 7 1 2 M e e e 6 7 6 7 F = [Fw Fw ] = 6 7 1 M . 6 . 7 6 . 7 4 . 5 jM1w jM 1w j M 1w 1 2 M e e e Look for the peaks in 2 jF x tj = jF xtj i To smooth out noise N X 1 2 jF xtj B w = i N t=1 7 Beamforming Algorithm Algorithm P N 1 R = xtx t 1. Estimate x t=1 N B w = F w R Fw i x i 2. Calculate i B w w i 3. Find peaks of i for all possible ’s. i = 1;::: ;d 4. Calculate k , . Advantage - Simple and easy to understand Disadvantage - Low resolution 8 Number of Sources d < M Detection of number of signals for , xt = Ast+ nt R = Efxtx tg = AEfsts tgA + E fntn tg x | {z } | {z } 2 R s I n 2 I = A R A + s n |{z} |{z} |{z} M d dM dd 2 where is the noise power. n R d No noise and rank of s is R = AR A s – Eigenvalues of x will be f ;::: ; ;0; ::: ;0g: 1 d R –Real positive eigenvalues because x is real, Hermition-symmetric – rank d R Check the rank of x or its nonzero eigenvalues to detect the number of signals 2 Noise eigenvalues are shifted by n 2 2 2 2 f + ;::: ; + ; ;::: ; g: 1 d n n n n > ::: > >> 0 1 where d and Detect the number of principal (distinct) eigenvalues 9 MUSIC Subspace decomposition by performing eigenvalue decomposition M X 2 I = e e R = AR A + x s k k n k k =1 e k where k is the eigenvector of the eigenvalue spanfAg = spanfe ;::: ;e g = spanfE g 1 d s a spanfE g P a A Check which s or or ? P a P , where A is a projection matrix A Search for all possible such that 1 ? 2 jP a j = 0 or M = = 1 A P a A R After EVD of x ? P = I E E = E E s n A s n where the noise eigenvector matrix E = [e ;::: ;e ] n d+1 M 10 Root-MUSIC j 2f 4 sin =c c e For a true , is a root of M X M1 T 1 M1 P z = [1;z;::: ;z ] e e [1;z ;::: ;z ]: k k k =d+1 After eigenvalue decomposition, d fe g - Obtain k k =1 z -Formp M 2 pz - Obtain 2 roots by rooting -Pickd roots lying on the unit circle f g - Solve for k 11 Estimation of Signal Parameters via Rotationally Invariant Techniques (ESPRIT) M Decompose a uniform linear array of sensors into 1 two subarrays with M sensors Note the shift invariance property 2 3 3 2 jw 1 e 6 7 7 6 6 7 7 6 jw j2w e e 6 7 7 6 jw 1 jw 2 6 7 7 6 = e = a e a = 6 7 7 6 . 6 7 7 6 . 4 5 5 4 jM 1w jM 1w e e General form relating subarray (1) to subarray (2) 2 3 jw 1 e 6 7 6 7 1 2 . = A1: = A A . 7 6 . 4 5 jw d e f g contains sufficient information of k 12 ESPRIT spanfE g = spanfAg E = AT s s and d d - T is a nonsingular unitary matrix - T comes from a Grahm-Schmit orthogonalization of Ab in R = E E + E E x s s n s n H 2 A R A + I s n 2 1 2 1 E = A T E = A T s s and 2 1 1 E 2 = A T = A T = E 1T T s s 1 E Multiply both sides by the pseudo inverse of s 1 1 1 1 1 1 1 1 E E 2 = E E E E T T = T T s s where means the pseudo-inverse sH 1 sH A = A A A 1 T Eigenvalues of T are those of . 13 Superresolution Algorithms P N 1 R = xk x k 1. Calculate x k =1 N 2. Perform eigenvalue decomposition f g d 3. Based on the distribution of k , determine 4. Use your favorite diraction-of-arrival estimation algorithm: 0 (a) MUSIC: Find the peaks of M for from to 180 d ^ f g d - Find k corresponding the peaks of k =1 M . z (b) Root-MUSIC: Root the polynomial p - Pick the d roots that are closest to the unit 1 r c d k ^ fr g = sin k circle k and . k =1 2f c 2 1 E E s (c) ESPRIT: Find the eigenvalues of s , f g k c 1 k ^ = sin - k 2f 4 c 14 Signal Waveform Estimation A st xt Given , recover from . Deterministic Method w – No noise case: find k such that w ? a ;i 6= k; w 6? a k i k k A can do the job A xt = A Ast = st nt With noise, A xt = st + A nt – Disadvantage = increased noise 15 Stocastic Approach w Find k to minimize 2 min E fjw xtj g = min w R w k k k k a w =1 a w =1 k k k k Use the Langrange method 2 min E fjw xtj g , min w R w + 2a w 1 k k k k k k ;w a w =1 k k k Differentiating it, we obtain 1 R w = a ;orw = R a x k k k k x . 1 a w = a R a = 1 k k k Since k , x Then 1 = a R a k k x Capon’s Beamformer 1 1 w = R a =a R a k k k k x x 16 Subspace Framework for Sinusoid Detection d P +j! t k k xt = e k k =1 M Let us select a window of , i.e., T xt = [xt;: : : ;xt M + 1] Then 2 3 3 2 +j! t k k xt e k 6 7 7 6 +j! t1 6 7 7 6 k k xt 1 e d k 6 7 7 6 X 6 7 7 6 xt = = 6 7 7 . 6 . 6 7 7 . 6 . 6 7 7 6 k=1 4 5 5 . 4 . +j! tM +1 k k xt M +1 e k 3 2 1 7 6 +j! 7 6 k k e d 7 6 X 7 6 +j! t k k e = 7 6 . k 7 6 {z } . | 7 6 k =1 5 4 . s t k +j! M +1 k k e {z } | a k d X a s t = Ast; = k k k =1 d where M is the window size, the number of sinusoids, and +j! k k = e k . 17 Subspace Framework for Sinusoid Detection Therefore, the subspace methods can be applied to f + j! g k find k Recall d X +j! t k k xt = e k k =1 f g Then finding k is a simple least squares problem. 18 Wireless Communications co-channel interference Multipaths Direct Path Cellular Telephony Office Building Residential Area Personal Communications Outdoors Services (PCS) To Networks Direct Path Direct Path Multipath Wireless LAN Increasing Demand for Wireless Services Unique Problems compared to Wired communications 19 Problems in Wireless Communications Scarce Radio Spectrum and Co-channel Interference 1 2 4 3 1 4 1 3 2 1 Multipath Multipath Direct Path Multipath Base Station Time Desired Signal Reflected Signal Coverage/Range 20 Smart Antenna Systems Employ more than one antenna element and exploit the spatial dimension in signal processing to improve some system operating parameter(s): - Capacity, Quality, Coverage, and Cost.
Load more

Recommended publications
  • Coarrays, MUSIC, and the Cramér Rao Bound
    1 Coarrays, MUSIC, and the Cramer´ Rao Bound Mianzhi Wang, Student Member, IEEE, and Arye Nehorai, Life Fellow, IEEE Abstract—Sparse linear arrays, such as co-prime arrays and ESPRIT [17]) via first-order perturbation analysis. However, nested arrays, have the attractive capability of providing en- these results are based on the physical array model and hanced degrees of freedom. By exploiting the coarray structure, make use of the statistical properties of the original sample an augmented sample covariance matrix can be constructed and MUSIC (MUtiple SIgnal Classification) can be applied to covariance matrix, which cannot be applied when the coarray identify more sources than the number of sensors. While such model is utilized. In [18], Gorokhov et al. first derived a a MUSIC algorithm works quite well, its performance has not general MSE expression for the MUSIC algorithm applied been theoretically analyzed. In this paper, we derive a simplified to matrix-valued transforms of the sample covariance matrix. asymptotic mean square error (MSE) expression for the MUSIC While this expression is applicable to coarray-based MUSIC, algorithm applied to the coarray model, which is applicable even if the source number exceeds the sensor number. We show that its explicit form is rather complicated, making it difficult to the directly augmented sample covariance matrix and the spatial conduct analytical performance studies. Therefore, a simpler smoothed sample covariance matrix yield the same asymptotic and more revealing MSE expression is desired. MSE for MUSIC. We also show that when there are more sources In this paper, we first review the coarray signal model than the number of sensors, the MSE converges to a positive commonly used for sparse linear arrays.
    [Show full text]
  • Iterative Sparse Asymptotic Minimum Variance Based Approaches For
    1 Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing Habti Abeida∗, Qilin Zhangy, Jian Liz and Nadjim Merabtinex ∗Department of Electrical Engineering, University of Taif, Al-Haweiah, Saudi Arabia yDepartment of Computer Science, Stevens Institue of Technology, Hoboken, NJ, USA zDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA xDepartment of Electrical Engineering, University of Taif, Al-Haweiah, Saudi Arabia Abstract This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to over- come the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar parameter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided at high signal-to-noise ratio (SNR) and low SNR, respectively. Finally, numerical examples are generated to compare the performance of the proposed approaches with existing approaches. Index terms: Array Processing, AMV estimator, Direction-Of-Arrival (DOA) estimation, Sparse parameter estima- tion, Covariance matrix, Iterative methods, Vectors, Arrays, Maximum likelihood estimation, Signal to noise ratio, SAMV approach.
    [Show full text]
  • Evaluation of MUSIC Algorithm for DOA Estimation in Smart Antenna
    IARJSET ISSN (Online) 2393-8021 ISSN (Print) 2394-1588 International Advanced Research Journal in Science, Engineering and Technology Vol. 3, Issue 5, May 2016 Evaluation of MUSIC algorithm for DOA estimation in Smart antenna Banuprakash.R1, H.Ganapathy Hebbar2, Sowmya.M3, Swetha.M4 Assistant Professor, Department of TE, BMS Institute of Technology and Management, Bengaluru, India1,2 Final Year Students, Department of TE, BMS Institute of Technology and Management, Bengaluru, India3,4 Abstract: This paper presents a simulation study of a smart antenna system on basis of direction-of-arrival estimation for identifying the direction of desired signal using Multiple Signal Classification (MUSIC) algorithm. In recent times smart antennas are gaining more popularity by increasing user capacity, achieved by effectively reducing interference. In this paper, performance and resolution analysis of the algorithm is made. Its sensitivity to various perturbations and the effect of parameters such as the number of array elements and the spacing between them which are related to the design of the array sensor are investigated. The analysis is based on uniform linear array antenna and the calculation of the pseudo spectrum function of the algorithm. MATLAB is used for simulating the algorithm. Keywords: Smart antenna, DOA, MUSIC, Pseudo spectrum, MATLAB. I. INTRODUCTION 1.1 DOA ESTIMATION ALGORITHM DOA is a research area in array signal processing and The requirement of mobile communication resources has many engineering application such as radar, radio been increased rapidly over few years. The smart astronomy, sonar and other emergency assistance devices techniques or adaptive beamforming techniques have been that need to be supported by estimation of DOA[2].
    [Show full text]
  • Antenna Array Processing: Autocalibration and Fast High-Resolution Methods for Automotive Radar
    Antenna Array Processing: Autocalibration and Fast High-Resolution Methods for Automotive Radar Vom Fachbereich 18 Elektrotechnik und Informationstechnik der Technischen Universit¨at Darmstadt zur Erlangung der W¨urde eines Doktor-Ingenieurs (Dr.-Ing.) genehmigte Dissertation von Dipl.-Ing. Philipp Heidenreich geboren am 27.12.1980 in R¨usselsheim Referent: Prof. Dr.-Ing. Abdelhak M. Zoubir Korreferent: Prof. Dr.-Ing. Bin Yang Tag der Einreichung: 29.02.2012 Tag der m¨undlichen Pr¨ufung: 06.06.2012 D 17 Darmstadt, 2012 I Danksagung Die vorliegende Arbeit entstand im Rahmen meiner T¨atigkeit als wissenschaftlicher Mitarbeiter am Fachgebiet Signalverarbeitung des Instituts f¨ur Nachrichtentechnik der Technischen Universit¨at Darmstadt. An dieser Stelle gilt mein Dank allen, die das Entstehen der Arbeit direkt oder indirekt erm¨oglicht haben. Besonders m¨ochte ich mich bei Prof. Dr.-Ing. Abdelhak Zoubir f¨ur die wissenschaftliche Betreuung der Arbeit bedanken. Seine stete Bereitschaft zur Diskussion und die F¨orderung von Publikationen und Projekten, sowie das Erm¨oglichen von Konferenz- reisen, haben maßgeblich zum Gelingen der Arbeit beigetragen. Des Weiteren bedanke ich mich bei Prof. Dr.-Ing Bin Yang f¨ur die freundliche Ubernahme¨ des Korreferats und sein Interesse an meiner Arbeit. Ebenso gilt mein Dank Prof. Dr.-Ing. Klaus Hofmann, Prof. Dr.-Ing. Rolf Jakoby und Prof. Dr.-Ing. habil. Tran Quoc Khanh f¨ur ihre Mitwirkung in der Pr¨ufungskommission. F¨ur die Kooperation und F¨orderung meiner Promotion m¨ochte ich mich bei der A.D.C. GmbH der Continental AG bedanken. Mein besonderer Dank gilt Florian Engels, Dr. Alexander Kaps, Dr. Peter Seydel und Dr.
    [Show full text]
  • Array Processing for Seismic Surface Waves
    Diss. ETH No. 21291 Array Processing for Seismic Surface Waves A dissertation submitted to ETH Zurich for the degree of Doctor of Sciences presented by Stefano Maranò Laurea Specialistica in Ingegneria delle Telecomunicazioni, Università degli Studi di Trento born on December 23, 1983 citizen of Italy accepted on the recommendation of Prof. Dr. Donat Fäh, examiner Prof. Dr. Hans-Andrea Loeliger, co-examiner Prof. Dr. Domenico Giardini, co-examiner Prof. Dr. Heiner Igel, co-examiner 2013 ii Abstract The analysis of seismic surface waves plays a major role in the under- standing of geological and geophysical features of the subsoil. Indeed seis- mic wave attributes such as velocity of propagation or wave polarization reflect the properties of the materials in which the wave is propagating. The analysis of properties of surface waves allows geophysicists to gain insight into the structure of the subsoil avoiding more expensive invasive techniques (e.g., borehole techniques). A myriad of applications benefit from the knowledge about the subsoil gained through seismic sur- veys. Microzonation studies are an important application of the analysis of surface waves with direct impact on damage mitigation and earthquake preparedness. This thesis aims at improving signal processing techniques for the analysis of surface waves in different directions. In particular, the main goal is to deliver accurate estimates of the geophysical parameters of interest. The availability of improved estimates of the quantities of in- terest will provide better constraints for the geophysical inversion and thus enabling us to obtain an improved structural earth model. For a rigorous treatment of the estimation of wavefield parameters we rely on tools from statistical signal processing.
    [Show full text]
  • Wideband Array Processing Algorithms for Acoustic Tracking of Ground Vehicles
    WIDEBAND ARRAY PROCESSING ALGORITHMS FOR ACOUSTIC TRACKING OF GROUND VEHICLES Tien Pham and Brian M. Sadler U. S. Army Research Laboratory Adelphi, MD 20783-1197 [email protected] Abstract Focused wideband adaptive array processing algorithms are used for the high-resolution aeroacoustic direction- finding of ground vehicles. Methods based on coherent focusing such as steered covariance matrix (STCM), and spatial smoothing or interpolation using the array manifold interpolation (AMI) method, will be discussed [1, 2]. Experimental results for a circular array are presented comparing and contrasting the coherent focused wideband methods with the incoherent wideband methods. Detailed analysis of the incoherent wideband MUSIC (IWM) algorithm versus the focused or coherent wideband MUSIC (CWM) algorithms shows processing gain over narrowband MUSIC in terms of accuracy and stability of the direction of arrival (DOA) estimates [3]. Given adequate SNR, incoherent wideband methods yield more accurate DOA estimates than focused methods for highly peaked spectra. On the other hand, the coherent wideband methods yield better DOA estimates for sources with flat spectra [4]. In general, the focused wideband methods are much more statistically stable than the incoherent wideband methods. In this paper, experimental results are shown for IWM, ICM, and AMI uniform circular array MUSIC (AMI-UCA MUSIC) algorithms using acoustic data collected from a 12- element UCA and computational complexity is discussed. 1. Introduction This paper describes work at the Army Research Laboratory (ARL) on array signal processing algorithms using small baseline acoustic arrays to passively detect, estimate direction of arrival (DOA), track, and classify moving targets for implementation in an acoustic sensor testbed [5].
    [Show full text]
  • Improved DOA Estimation in KR-MUSIC Algorithm with Non-Uniform Noise
    Improved DOA Estimation in KR-MUSIC Algorithm with Non-Uniform Noise Abhishek Aich Department of Electrical and Computer Engineering University of California Riverside, CA 92507 [email protected] Abstract The Khatri-Rao (KR)-MUSIC1 algorithm for Direction of Arrival (DOA) estima- tion of quasi-stationary signals in the presence of non-uniform noise doesn’t ex- plicitly estimate the noise covariance matrix. Hence, the method performs poorly in low SNR environment as the effect of covariance between noise and signal vectors increases on the data covariance matrix. The uniform linear array DOA model in KR-subspace follows the sparse representation in spatial domain for all frames of the quasi-stationary signals. In this paper, we propose a modified de- noising paradigm to improve the DOA estimation of the algorithm by using this sparse representation in KR-subspace. The sparse representation is utilized to perform covariance fitting to eliminate noise from the data model. Numerical experiment further demonstrate the effectiveness of the proposed method. An implementation of the proposed algorithm can be found in the following link: http://tinyurl.com/ee260-aich 1 Introduction For several decades, array signal processing has been of keen interest in variety of applications, such as radar, sonar, radio astronomy, etc. [1]. Major state-of-the-art classical algorithms have been pro- posed over the past few decades, such as MUltiple SIgnal Classification (MUSIC) [2] and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) [3]. Such algorithms require a compact sensor array in order to avoid the problem of angle ambiguity as these algorithms use noise/signal subspace analysis for DOA estimation.
    [Show full text]
  • Principles of Adaptive Array Processing
    Principles of Adaptive Array Processing Dr. Ulrich Nickel Research Institute for High-Frequency Physics and Radar Techniques (FHR), Research Establishment for Applied Science (FGAN), 53343 Wachtberg, Germany [email protected] ABSTRACT In this lecture we present the principles of adaptive beamforming, the problem of estimating the adaptive weights and several associated practical problems, like preserving low sidelobe patterns, using subarrays and GSLC configurations. We explain the detection problem with adaptive arrays and the methods for angle estimation. Finally the methods for resolution enhancement (super-resolution methods) are presented. 1.0 INTRODUCTION – WHY DO WE NEED ADAPTIVE BEAMFORMING By adaptive beamforming we mean a data dependent modification of the antenna pattern. We are talking here about two types of data: the training data from which we calculate the adaptive weightings and the primary data with which detection and parameter estimation (angle, range, Doppler estimation) is performed. In this section we consider spatial pattern forming only. However, our description is more general to allow the extension to space-time adaptive processing to be developed in the following lectures. First, one may ask if adaptive beamforming against spatial interference sources is necessary at all, if we can create an antenna with very low sidelobes. A low sidelobe antenna is a fixed spatial filter that does not need training and hence has no adaptation problems. However, the overall reduction of the sidelobes implies an increase of the antenna beamwidth. This effect is not present if single nulls in the directions of the interference are formed. Often the detection and estimation of a target in the vicinity of the interference is of interest (from a jamming point of view co-locating the jammer and the target to be protected is most efficient).
    [Show full text]
  • Signal Processing in Space and Time - a Multidimensional Fourier Approach
    Signal Processing in Space and Time - A Multidimensional Fourier Approach THÈSE NO 4871 (2010) PRÉSENTÉE LE 3 DÉCEMBRE 2010 À LA FACULTÉ INFORMATIQUE ET COMMUNICATIONS LABORATOIRE DE COMMUNICATIONS AUDIOVISUELLES 1 PROGRAMME DOCTORAL EN INFORMATIQUE, COMMUNICATIONS ET INFORMATION ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES PAR Francisco PEREIRA CORREIA PINTO acceptée sur proposition du jury: Prof. E. Telatar, président du jury Prof. M. Vetterli, directeur de thèse Prof. D. L. Jones, rapporteur Prof. J. M. F. Moura, rapporteur Prof. M. Unser, rapporteur Suisse 2010 Abstract Sound waves propagate through space and time by transference of energy between the parti- cles in the medium, which vibrate according to the oscillation patterns of the waves. These vibrations can be captured by a microphone and translated into a digital signal, represent- ing the amplitude of the sound pressure as a function of time. The signal obtained by the microphone characterizes the time-domain behavior of the acoustic wave field, but has no information related to the spatial domain. The spatial information can be obtained by mea- suring the vibrations with an array of microphones distributed at multiple locations in space. This allows the amplitude of the sound pressure to be represented not only as a function of time but also as a function of space. The use of microphone arrays creates a new class of signals that is somewhat unfamiliar to Fourier analysis. Current paradigms try to circumvent the problem by treating the micro- phone signals as multiple “cooperating” signals, and applying the Fourier analysis to each signal individually.
    [Show full text]
  • A Multi-Microphone Approach to Speech Processing in a Smart-Room Environment
    PhD Thesis A multi-microphone approach to speech processing in a smart-room environment Author: Alberto Abad Gareta Advisor: Dr. Fco. Javier Hernando Peric´as Speech Processing Group Department of Signal Theory and Communications Universitat Polit`ecnica de Catalunya Barcelona, February 2007 A mis padres y hermana, Abstract Recent advances in computer technology and speech and language processing have made possi- ble that some new ways of person-machine communication and computer assistance to human activities start to appear feasible. Concretely, the interest on the development of new challenging applications in indoor environments equipped with multiple multimodal sensors, also known as smart-rooms, has considerably grown. In general, it is well-known that the quality of speech signals captured by microphones that can be located several meters away from the speakers is severely distorted by acoustic noise and room reverberation. In the context of the development of hands-free speech applications in smart-room environments, the use of obtrusive sensors like close-talking microphones is usually not allowed, and consequently, speech technologies must operate on the basis of distant-talking recordings. In such conditions, speech technologies that usually perform reasonably well in free of noise and reverberation environments show a dramatically drop of performance. In this thesis, the use of a multi-microphone approach to solve the problems introduced by far-field microphones in speech applications deployed in smart-rooms is investigated. Con- cretely, microphone array processing is investigated as a possible way to take advantage of the multi-microphone availability in order to obtain enhanced speech signals. Microphone array beamforming permits targeting concrete desired spatial directions while others are rejected, by means of the appropriate combination of the signals impinging a microphone array.
    [Show full text]
  • Array Processing for Radar: Achievements and Challenges
    Hindawi Publishing Corporation International Journal of Antennas and Propagation Volume 2013, Article ID 261230, 21 pages http://dx.doi.org/10.1155/2013/261230 Research Article Array Processing for Radar: Achievements and Challenges Ulrich Nickel Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE), Fraunhoferstrasse 20, 53343 Wachtberg, Germany Correspondence should be addressed to Ulrich Nickel; [email protected] Received 26 March 2013; Accepted 26 July 2013 Academic Editor: Hang Hu Copyright © 2013 Ulrich Nickel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Array processing for radar is well established in the literature, but only few of these algorithms have been implemented in real systems. The reason may be that the impact of these algorithms on the overall system must be well understood. For a successful implementation of array processing methods exploiting the full potential, the desired radar task has to be considered and all processing necessary for this task has to be eventually adapted. In this tutorial paper, we point out several viewpoints which are relevant in this context: the restrictions and the potential provided by different array configurations, the predictability of the transmission function of the array, the constraints for adaptive beamforming, the inclusion of monopulse, detection and tracking into the adaptive beamforming concept, and the assessment of superresolution methods with respect to their application in a radar system. The problems and achieved results are illustrated by examples from previous publications. 1. Introduction between array processing and preceding and subsequent radar processing.
    [Show full text]
  • An Efficient High-Dimensional Sparse Fourier Transform
    1 An Efficient High-Dimensional Sparse Fourier Transform Shaogang Wang, Student Member, IEEE, Vishal M. Patel, Senior Member, IEEE, and Athina Petropulu, Fellow, IEEE Abstract We propose RSFT, which is an extension of the one dimensional Sparse Fourier Transform algorithm to higher dimensions in a way that it can be applied to real, noisy data. The RSFT allows for off-grid frequencies. Furthermore, by incorporating Neyman-Pearson detection, the frequency detection stages in RSFT do not require knowledge of the exact sparsity of the signal and are more robust to noise. We analyze the asymptotic performance of RSFT, and study the computational complexity versus the worst case signal SNR tradeoff. We show that by choosing the proper parameters, the optimal tradeoff can be achieved. We discuss the application of RSFT on short range ubiquitous radar signal processing, and demonstrate its feasibility via simulations. Index Terms Array signal processing, sparse Fourier transform, detection and estimation, radar signal processing. I. INTRODUCTION High dimensional FFT (N-D FFT) is used in many applications in which a multidimensional Discrete Fourier Transform (DFT) is needed, such as radar and imaging. However, its complexity increases with an increase in dimension, leading to costly hardware and low speed of system reaction. The recently developed Sparse Fourier Transform (SFT) [1]–[6], designed for signals that have a small number of frequencies enjoys low complexity, and arXiv:1610.01050v1 [cs.SY] 4 Oct 2016 thus is ideally suited in big data scenarios [7]–[9]. One such application is radar target detection. The radar returns are typically sparse in the target space, i.e., the number of targets is far less than the number of resolutions cells.
    [Show full text]