Coprime Coarray Interpolation for DOA Estimation via Nuclear Norm Minimization
|
|
- Kelly Weaver
- 5 years ago
- Views:
Transcription
1 Coprime Coarray Interpolation for DOA Estimation via Nuclear Norm Minimization Chun-Lin Liu 1 P. P. Vaidyanathan 2 Piya Pal 3 1,2 Dept. of Electrical Engineering, MC California Institute of Technology, cl.liu@caltech.edu 1, ppvnath@systems.caltech.edu 2 3 Dept. of Electrical and Computer Engineering University of Maryland, College Park ppal@umd.edu ISCAS 2016 Liu et al. Coprime Coarray Interpolation ISCAS / 19
2 Outline 1 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) 2 Coarray Interpolation via Nuclear Norm Minimization 3 Numerical Examples 4 Concluding Remarks Liu et al. Coprime Coarray Interpolation ISCAS / 19
3 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) Outline 1 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) 2 Coarray Interpolation via Nuclear Norm Minimization 3 Numerical Examples 4 Concluding Remarks Liu et al. Coprime Coarray Interpolation ISCAS / 19
4 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) Direction-of-arrival (DOA) estimation 1 θ i Monochromatic Uncorrelated Sources Sensor Arrays DOA Estimators Estimated DOA θ i 1 Van Trees, Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory, Liu et al. Coprime Coarray Interpolation ISCAS / 19
5 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) ULA and sparse arrays ULA (not sparse) Identify at most N 1 uncorrelated sources, given N sensors. 1 Can only find fewer sources than sensors. Sparse arrays 1 Minimum redundancy arrays 2 2 Nested arrays 3 3 Coprime arrays 4 4 Super nested arrays 5 Identify O(N 2 ) uncorrelated sources with O(N) physical sensors. More sources than sensors! 1 Van Trees, Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory, Moffet, IEEE Trans. Antennas Propag., Pal and Vaidyanathan, IEEE Trans. Signal Proc., Vaidyanathan and Pal, IEEE Trans. Signal Proc., Liu and Vaidyanathan, IEEE Trans. Signal Proc., Liu et al. Coprime Coarray Interpolation ISCAS / 19
6 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) Coprime arrays 1 The coprime array with (M, N) = 1 is the union of 1 an N-element ULA with spacing Mλ/2 and 2 a 2M-element ULA with spacing Nλ/2. ULA (1) ULA (2) Physical array S (M = 3, N = 4): Difference coarray D = {n 1 n 2 n 1, n 2 S} Holes 1 Vaidyanathan and Pal, IEEE Trans. Signal Proc., Liu et al. Coprime Coarray Interpolation ISCAS / 19
7 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) The spatial smoothing MUSIC Algorithm 1 1 Sample covariance matrix: R S = 1 K K k=1 x S(k) x H S (k). 2 Sample autocorrelation function on the difference coarray: x D. x D x U Hermitian Toeplitz matrix R (indefinite matrix). x U 0 x U 1... x U 14 x U 1 x U 0... x U 13 R = x U 14 x U x U 0 DU 4 MUSIC on R resolves ( U 1)/2 = O(N 2 ) uncorrelated sources. 1 Pal and Vaidyanathan, IEEE Trans. Signal Proc., 2010; Liu and Vaidyanathan, IEEE Signal Proc. Letter, Liu et al. Coprime Coarray Interpolation ISCAS / 19
8 Outline Coarray Interpolation via Nuclear Norm Minimization 1 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) 2 Coarray Interpolation via Nuclear Norm Minimization 3 Numerical Examples 4 Concluding Remarks Liu et al. Coprime Coarray Interpolation ISCAS / 19
9 Coarray Interpolation via Nuclear Norm Minimization Why coarray interpolation? x D D D = 3MN + M N Not all information is used. x U U U = 2MN + 2M 1 All information is used. x V V V = 4MN 2N + 1 Liu et al. Coprime Coarray Interpolation ISCAS / 19
10 Coarray Interpolation via Nuclear Norm Minimization Previous work 1 Spatial smoothing MUSIC 1 : No coarray interpolation. 2 Positive-definite Toeplitz matrix completion 2 : Not always feasible. 3 Coarray interpolation (ICA-AI) 3 : Non-convex optimization. 4 Sparse support recovery techniques 4 : Predefined dense grid and parameters. 5 Gridless DOA estimator via low-rank recovery 5 : Not used for interpolation, but for denoising. 1 Pal and Vaidyanathan, IEEE Trans. Signal Proc., 2010; Liu and Vaidyanathan, IEEE Signal Proc. Letter, Abramovich, Spencer, and Gorokhov, IEEE Trans. Signal Proc., Friedlander and Weiss, IEEE Trans. Aero. Elec. Sys., 1992; Tuncer, Yasar, and Friedlander, Radio Science, Zhang, Amin, and Himed, IEEE ICASSP, 2013; Pal and Vaidyanathan, IEEE Trans. Signal Proc., 2015; 5 Pal and Vaidyanathan, IEEE Signal Proc. Letter, Liu et al. Coprime Coarray Interpolation ISCAS / 19
11 Coarray Interpolation via Nuclear Norm Minimization The proposed method (via nuclear norm minimization) R V = arg min R V C V+ V + R V = R H V, R V s. t. R V n1,n 2 = x D n1 n 2, n 1, n 2 V + = {n n V, n 0}. x D D x V autocorrelation functions V R V covariance matrices Low rank, Hermitian Toeplitz R V has a low-rank structure for sufficient number of snapshots. The nuclear norm (sum of singular values) is a convex relaxation of the matrix rank. R V is Hermitian. R V is a Toeplitz matrix with some known entries. Liu et al. Coprime Coarray Interpolation ISCAS / 19
12 Coarray Interpolation via Nuclear Norm Minimization Advantages over the previous work 1 All the information is used. 2 Gridless. 3 Always feasible, even though R V can be indefinite. 4 Convex program. 5 It is possible to resolve beyond the limit of U. x V autocorrelation functions V R V covariance matrices Low rank, Hermitian Toeplitz Coarray interpolation R V = arg min R V subject to MUSIC R V C V+ V + R V = R H V, P MUSIC () = R V n1,n 2 = x D n1 n 2. R V = ŨΛŨH, ] Ũ = [Ũs Ũ n, 1 ŨH n v V +() 2 2 Liu et al. Coprime Coarray Interpolation ISCAS / 19
13 Numerical Examples Outline 1 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) 2 Coarray Interpolation via Nuclear Norm Minimization 3 Numerical Examples 4 Concluding Remarks Liu et al. Coprime Coarray Interpolation ISCAS / 19
14 Numerical Examples Simulation parameters A coprime array with M = 3 and N = 5: (10 sensors) S = {0, 3, 5, 6, 9, 10, 12, 15, 20, 25}, S = 10, S 1 = 9, D = { 25, 22, 20, 19, 17,..., 17, 19, 20, 22, 25}, D = 43, ( D 1)/2 = 21, U = { 17,..., 17}, U = 35, ( U 1)/2 = 17, V = { 25,..., 25}, V = 51. S : D : U : V : Liu et al. Coprime Coarray Interpolation ISCAS / 19
15 Numerical Examples Simulation parameters (Cont.) 1 The maximum number of resolvable uncorrelated sources: using U: 17, using D: Equal-power uncorrelated sources. 3 0 db SNR and 500 snapshots. 4 Root-mean-squared error: E = 1 D D ( i ) 2, i i=1 where { i } D i=1 is the estimated normalized DOA, and { i } D i=1 is the true normalized DOA. Liu et al. Coprime Coarray Interpolation ISCAS / 19
16 Numerical Examples Example 1: Two closely spaced sources (10 sensors) SS-MUSIC 1 Co-LASSO 4 Spline 3 P () E ICA-AI 3 P.D. Toeplitz Completion 2 Nuclear norm (Proposed) P () E See page 10 for all the references Liu et al. Coprime Coarray Interpolation ISCAS / 19
17 Numerical Examples Example 2: D = 19 sources (10 sensors) Co-LASSO (E = ) Spline (E = ) ICA-AI (E = ) Nuclear norm (E = ) Liu et al. Coprime Coarray Interpolation ISCAS / 19
18 Concluding Remarks Outline 1 Introduction (DOA, Coprime Arrays, Spatial Smoothing MUSIC) 2 Coarray Interpolation via Nuclear Norm Minimization 3 Numerical Examples 4 Concluding Remarks Liu et al. Coprime Coarray Interpolation ISCAS / 19
19 Concluding remarks Concluding Remarks Coarray interpolation via nuclear norm minimization: The correlation information on the difference coarray is fully utilized. The estimation error is reduced. More sources than ( U 1)/2 (the limit of SS MUSIC using x U ) can be resolved. In the future, it will be interesting to incorporate the matrix denoising idea into the interpolation approach. Thank you! Liu et al. Coprime Coarray Interpolation ISCAS / 19
New Cramér-Rao Bound Expressions for Coprime and Other Sparse Arrays
New Cramér-Rao Bound Expressions for Coprime and Other Sparse Arrays Chun-Lin Liu 1 P. P. Vaidyanathan 2 1,2 Dept. of Electrical Engineering, MC 136-93 California Institute of Technology, Pasadena, CA
More informationTensor MUSIC in Multidimensional Sparse Arrays
Tensor MUSIC in Multidimensional Sparse Arrays Chun-Lin Liu 1 and P. P. Vaidyanathan 2 Dept. of Electrical Engineering, MC 136-93 California Institute of Technology, Pasadena, CA 91125, USA cl.liu@caltech.edu
More informationTwo-Dimensional Sparse Arrays with Hole-Free Coarray and Reduced Mutual Coupling
Two-Dimensional Sparse Arrays with Hole-Free Coarray and Reduced Mutual Coupling Chun-Lin Liu and P. P. Vaidyanathan Dept. of Electrical Engineering, 136-93 California Institute of Technology, Pasadena,
More informationCo-prime Arrays with Reduced Sensors (CARS) for Direction-of-Arrival Estimation
Co-prime Arrays with Reduced Sensors (CARS) for Direction-of-Arrival Estimation Mingyang Chen 1,LuGan and Wenwu Wang 1 1 Department of Electrical and Electronic Engineering, University of Surrey, U.K.
More informationEXTENSION OF NESTED ARRAYS WITH THE FOURTH-ORDER DIFFERENCE CO-ARRAY ENHANCEMENT
EXTENSION OF NESTED ARRAYS WITH THE FOURTH-ORDER DIFFERENCE CO-ARRAY ENHANCEMENT Qing Shen,2, Wei Liu 2, Wei Cui, Siliang Wu School of Information and Electronics, Beijing Institute of Technology Beijing,
More informationROBUSTNESS OF COARRAYS OF SPARSE ARRAYS TO SENSOR FAILURES
ROBUSTNESS OF COARRAYS OF SPARSE ARRAYS TO SENSOR FAILURES Chun-Lin Liu 1 and P. P. Vaidyanathan 2 Dept. of Electrical Engineering, MC 136-93 California Institute of Technology, Pasadena, CA 91125, USA
More informationONE-BIT SPARSE ARRAY DOA ESTIMATION
ONE-BIT SPARSE ARRAY DOA ESTIMATION Chun-Lin Liu 1 and P. P. Vaidyanathan 2 Dept. of Electrical Engineering, 136-93 California Institute of Technology, Pasadena, CA 91125, USA E-mail: cl.liu@caltech.edu
More informationPERFORMANCE ANALYSIS OF COARRAY-BASED MUSIC AND THE CRAMÉR-RAO BOUND. Mianzhi Wang, Zhen Zhang, and Arye Nehorai
PERFORANCE ANALYSIS OF COARRAY-BASED USIC AND THE CRAÉR-RAO BOUND ianzhi Wang, Zhen Zhang, and Arye Nehorai Preston. Green Department of Electrical and Systems Engineering, Washington University in St.
More informationPerformance Analysis of Coarray-Based MUSIC and the Cramér-Rao Bound
Performance Analysis of Coarray-Based MUSIC and the Cramér-Rao Bound Mianzhi Wang, Zhen Zhang, and Arye Nehorai Preston M. Green Department of Electrical & Systems Engineering Washington University in
More informationGeneralized Design Approach for Fourth-order Difference Co-array
Generalized Design Approach for Fourth-order Difference Co-array Shiwei Ren, Tao Zhu, Jianyan Liu School of Information and Electronics,Beijing Institute of Technology, Beijing 8, China renshiwei@bit.edu.cn,zhutao@bit.edu.cn
More informationAn Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising
sensors Article An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising Muran Guo 1,3, Tao Chen 1, * and Ben Wang 2,3 1 College of Information and Communication Engineering,
More informationReal-Valued Khatri-Rao Subspace Approaches on the ULA and a New Nested Array
Real-Valued Khatri-Rao Subspace Approaches on the ULA and a New Nested Array Huiping Duan, Tiantian Tuo, Jun Fang and Bing Zeng arxiv:1511.06828v1 [cs.it] 21 Nov 2015 Abstract In underdetermined direction-of-arrival
More informationDesign of Coprime DFT Arrays and Filter Banks
Design o Coprime DFT Arrays and Filter Banks Chun-Lin Liu clliu@caltechedu P P Vaidyanathan ppvnath@systemscaltechedu Digital Signal Processing Group Electrical Engineering Caliornia Institute o Technology
More informationCorrelation Subspaces: Generalizations and Connection to Difference Coarrays
Correlation Subspaces: Generalizations and Connection to Difference Coarrays Chun-Lin Liu, Student Member, IEEE, and P. P. Vaidyanathan, Fellow, IEEE Abstract Direction-of-arrival (DOA) estimation finds
More informationThe Mean-Squared-Error of Autocorrelation Sampling in Coprime Arrays
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (IEEE CAMSAP 27), Curacao, Dutch Antilles, Dec. 27. The Mean-Squared-Error of Autocorrelation Sampling in Coprime
More informationSPARSITY-BASED ROBUST ADAPTIVE BEAMFORMING EXPLOITING COPRIME ARRAY
SPARSITY-BASED ROBUST ADAPTIVE BEAMFORMING EXPLOITING COPRIME ARRAY K. Liu 1,2 and Y. D. Zhang 2 1 College of Automation, Harbin Engineering University, Harbin 151, China 2 Department of Electrical and
More informationDOA Estimation Using Triply Primed Arrays Based on Fourth-Order Statistics
Progress In Electromagnetics Research M, Vol. 67, 55 64, 218 DOA Estimation Using Triply Primed Arrays Based on Fourth-Order Statistics Kai-Chieh Hsu and Jean-Fu Kiang * Abstract A triply primed array
More informationCo-Prime Arrays and Difference Set Analysis
7 5th European Signal Processing Conference (EUSIPCO Co-Prime Arrays and Difference Set Analysis Usham V. Dias and Seshan Srirangarajan Department of Electrical Engineering Bharti School of Telecommunication
More informationAdaptive beamforming for uniform linear arrays with unknown mutual coupling. IEEE Antennas and Wireless Propagation Letters.
Title Adaptive beamforming for uniform linear arrays with unknown mutual coupling Author(s) Liao, B; Chan, SC Citation IEEE Antennas And Wireless Propagation Letters, 2012, v. 11, p. 464-467 Issued Date
More informationOn the Robustness of Co-prime Sampling
3rd European Signal Processing Conference EUSIPCO On the Robustness of Co-prime Sampling Ali Koochakzadeh Dept. of Electrical and Computer Engineering University of Maryland, College Park Email: alik@umd.edu
More informationPerformance Analysis for Strong Interference Remove of Fast Moving Target in Linear Array Antenna
Performance Analysis for Strong Interference Remove of Fast Moving Target in Linear Array Antenna Kwan Hyeong Lee Dept. Electriacal Electronic & Communicaton, Daejin University, 1007 Ho Guk ro, Pochen,Gyeonggi,
More informationDOA Estimation of Uncorrelated and Coherent Signals in Multipath Environment Using ULA Antennas
DOA Estimation of Uncorrelated and Coherent Signals in Multipath Environment Using ULA Antennas U.Somalatha 1 T.V.S.Gowtham Prasad 2 T. Ravi Kumar Naidu PG Student, Dept. of ECE, SVEC, Tirupati, Andhra
More informationCramér-Rao Bounds for Coprime and Other Sparse Arrays, which Find More Sources than Sensors
Cramér-Rao Bounds for Coprime and Other parse Arrays, which Find More ources than ensors Chun-Lin Liu, P. P. Vaidyanathan ept. of Electrical Engineering, MC 136-93, California Institute of Technology,
More informationA ROBUST BEAMFORMER BASED ON WEIGHTED SPARSE CONSTRAINT
Progress In Electromagnetics Research Letters, Vol. 16, 53 60, 2010 A ROBUST BEAMFORMER BASED ON WEIGHTED SPARSE CONSTRAINT Y. P. Liu and Q. Wan School of Electronic Engineering University of Electronic
More informationA New High-Resolution and Stable MV-SVD Algorithm for Coherent Signals Detection
Progress In Electromagnetics Research M, Vol. 35, 163 171, 2014 A New High-Resolution and Stable MV-SVD Algorithm for Coherent Signals Detection Basma Eldosouky, Amr H. Hussein *, and Salah Khamis Abstract
More informationSparse Sensing in Colocated MIMO Radar: A Matrix Completion Approach
Sparse Sensing in Colocated MIMO Radar: A Matrix Completion Approach Athina P. Petropulu Department of Electrical and Computer Engineering Rutgers, the State University of New Jersey Acknowledgments Shunqiao
More informationFast 2-D Direction of Arrival Estimation using Two-Stage Gridless Compressive Sensing
Fast 2-D Direction of Arrival Estimation using Two-Stage Gridless Compressive Sensing Mert Kalfa ASELSAN Research Center ASELSAN Inc. Ankara, TR-06370, Turkey Email: mkalfa@aselsan.com.tr H. Emre Güven
More informationSelf-Calibration and Biconvex Compressive Sensing
Self-Calibration and Biconvex Compressive Sensing Shuyang Ling Department of Mathematics, UC Davis July 12, 2017 Shuyang Ling (UC Davis) SIAM Annual Meeting, 2017, Pittsburgh July 12, 2017 1 / 22 Acknowledgements
More informationDIRECTION OF ARRIVAL ESTIMATION BASED ON FOURTH-ORDER CUMULANT USING PROPAGATOR METHOD
Progress In Electromagnetics Research B, Vol. 18, 83 99, 2009 DIRECTION OF ARRIVAL ESTIMATION BASED ON FOURTH-ORDER CUMULANT USING PROPAGATOR METHOD P. Palanisamy and N. Rao Department of Electronics and
More informationDirection of Arrival Estimation: Subspace Methods. Bhaskar D Rao University of California, San Diego
Direction of Arrival Estimation: Subspace Methods Bhaskar D Rao University of California, San Diego Email: brao@ucsdedu Reference Books and Papers 1 Optimum Array Processing, H L Van Trees 2 Stoica, P,
More informationOn the Application of the Global Matched Filter to DOA Estimation with Uniform Circular Arrays
702 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 49, NO. 4, APRIL 2001 On the Application of the Global Matched Filter to DOA Estimation with Uniform Circular Arrays Jean-Jacques Fuchs, Member, IEEE Abstract
More informationIEEE SENSORS JOURNAL, VOL. 17, NO. 3, FEBRUARY 1,
IEEE SENSORS JOURNAL, VOL. 17, NO. 3, FEBRUARY 1, 2017 755 Source Estimation Using Coprime Array: A Sparse Reconstruction Perspective Zhiguo Shi, Senior Member, IEEE, Chengwei Zhou, Student Member, IEEE,
More informationTHE estimation of covariance matrices is a crucial component
1 A Subspace Method for Array Covariance Matrix Estimation Mostafa Rahmani and George K. Atia, Member, IEEE, arxiv:1411.0622v1 [cs.na] 20 Oct 2014 Abstract This paper introduces a subspace method for the
More informationADAPTIVE ANTENNAS. SPATIAL BF
ADAPTIVE ANTENNAS SPATIAL BF 1 1-Spatial reference BF -Spatial reference beamforming may not use of embedded training sequences. Instead, the directions of arrival (DoA) of the impinging waves are used
More informationDOA Estimation of Quasi-Stationary Signals Using a Partly-Calibrated Uniform Linear Array with Fewer Sensors than Sources
Progress In Electromagnetics Research M, Vol. 63, 185 193, 218 DOA Estimation of Quasi-Stationary Signals Using a Partly-Calibrated Uniform Linear Array with Fewer Sensors than Sources Kai-Chieh Hsu and
More informationSparse Parameter Estimation: Compressed Sensing meets Matrix Pencil
Sparse Parameter Estimation: Compressed Sensing meets Matrix Pencil Yuejie Chi Departments of ECE and BMI The Ohio State University Colorado School of Mines December 9, 24 Page Acknowledgement Joint work
More informationResearch Article Novel Diagonal Reloading Based Direction of Arrival Estimation in Unknown Non-Uniform Noise
Mathematical Problems in Engineering Volume 2018, Article ID 3084516, 9 pages https://doi.org/10.1155/2018/3084516 Research Article Novel Diagonal Reloading Based Direction of Arrival Estimation in Unknown
More informationGeneralization Propagator Method for DOA Estimation
Progress In Electromagnetics Research M, Vol. 37, 119 125, 2014 Generalization Propagator Method for DOA Estimation Sheng Liu, Li Sheng Yang, Jian ua uang, and Qing Ping Jiang * Abstract A generalization
More informationDESIGN AND ANALYSIS OF MULTI-COSET ARRAYS
DESIGN AND ANALYSIS OF MULTI-COSET ARRAYS James D. Krieger?, Yuval Kochman, and Gregory W. Wornell?? Dept. EECS, Massachusetts Institute of Technology, Cambridge, MA 39, USA School of CSE, Hebrew University
More informationThreshold Effects in Parameter Estimation from Compressed Data
PAKROOH et al.: THRESHOLD EFFECTS IN PARAMETER ESTIMATION FROM COMPRESSED DATA 1 Threshold Effects in Parameter Estimation from Compressed Data Pooria Pakrooh, Student Member, IEEE, Louis L. Scharf, Life
More informationA Novel DOA Estimation Error Reduction Preprocessing Scheme of Correlated Waves for Khatri-Rao Product Extended-Array
IEICE TRANS. COMMUN., VOL.E96 B, NO.0 OCTOBER 203 2475 PAPER Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP202 A Novel DOA Estimation Error Reduction
More information2458 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 46, NO. 9, SEPTEMBER 1998
2458 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL 46, NO 9, SEPTEMBER 1998 Positive-Definite Toeplitz Completion in DOA Estimation for Nonuniform Linear Antenna Arrays Part I: Fully Augmentable Arrays Yuri
More informationJoint Direction-of-Arrival and Order Estimation in Compressed Sensing using Angles between Subspaces
Aalborg Universitet Joint Direction-of-Arrival and Order Estimation in Compressed Sensing using Angles between Subspaces Christensen, Mads Græsbøll; Nielsen, Jesper Kjær Published in: I E E E / S P Workshop
More informationThis is a repository copy of Direction finding and mutual coupling estimation for uniform rectangular arrays.
This is a repository copy of Direction finding and mutual coupling estimation for uniform rectangular arrays. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/90492/ Version:
More informationarxiv: v2 [cs.it] 7 Jan 2017
Sparse Methods for Direction-of-Arrival Estimation Zai Yang, Jian Li, Petre Stoica, and Lihua Xie January 10, 2017 arxiv:1609.09596v2 [cs.it] 7 Jan 2017 Contents 1 Introduction 3 2 Data Model 4 2.1 Data
More informationSpatial Smoothing and Broadband Beamforming. Bhaskar D Rao University of California, San Diego
Spatial Smoothing and Broadband Beamforming Bhaskar D Rao University of California, San Diego Email: brao@ucsd.edu Reference Books and Papers 1. Optimum Array Processing, H. L. Van Trees 2. Stoica, P.,
More informationSingle-Stage Transmit Beamforming Design for MIMO Radar
Single-Stage Transmit Beamforming Design for MIMO Radar Mojtaba Soltanalian* a, Heng Hu b, and Petre Stoica a a Dept. of Information Technology, Uppsala University, Uppsala, Sweden b School of Electronic
More informationABSTRACT. arrays with perturbed sensor locations (with unknown perturbations). Simplified
ABSTRACT Title of Thesis: Performance and Robustness Analysis of Co-Prime and Nested Sampling Ali Koochakzadeh, Master of Science, 2016 Thesis Directed By: Professor Piya Pal Department of Electrical and
More informationUnderdetermined DOA Estimation Using MVDR-Weighted LASSO
Article Underdetermined DOA Estimation Using MVDR-Weighted LASSO Amgad A. Salama, M. Omair Ahmad and M. N. S. Swamy Department of Electrical and Computer Engineering, Concordia University, Montreal, PQ
More informationA Root-MUSIC-Like Direction Finding Method for Cyclostationary Signals
EURASIP Journal on Applied Signal Processing 25:1, 69 73 c 25 Hindawi Publishing Corporation A Root-MUSIC-Like Direction Finding Method for Cyclostationary Signals Pascal Chargé LESIA, DGEI, INSA Toulouse,
More informationRobust Subspace DOA Estimation for Wireless Communications
Robust Subspace DOA Estimation for Wireless Communications Samuli Visuri Hannu Oja ¾ Visa Koivunen Laboratory of Signal Processing Computer Technology Helsinki Univ. of Technology P.O. Box 3, FIN-25 HUT
More informationRobust Adaptive Beamforming via Estimating Steering Vector Based on Semidefinite Relaxation
Robust Adaptive Beamforg via Estimating Steering Vector Based on Semidefinite Relaxation Arash Khabbazibasmenj, Sergiy A. Vorobyov, and Aboulnasr Hassanien Dept. of Electrical and Computer Engineering
More information2542 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 62, NO. 10, MAY 15, Keyong Han, Student Member, IEEE, and Arye Nehorai, Fellow, IEEE
2542 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 62, NO. 10, MAY 15, 2014 Nested Vector-Sensor Array Processing via Tensor Modeling Keyong Han, Student Member, IEEE, and Arye Nehorai, Fellow, IEEE Abstract
More informationRoot-MUSIC Time Delay Estimation Based on Propagator Method Bin Ba, Yun Long Wang, Na E Zheng & Han Ying Hu
International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 15) Root-MUSIC ime Delay Estimation Based on ropagator Method Bin Ba, Yun Long Wang, Na E Zheng & an Ying
More informationTwo-Dimensional Parametric Models for Signal Processing of Data Stationary in 1 D or 2 D: Maximum-Entropy Extensions and Time-Varying Relaxations
Two-Dimensional Parametric Models for Signal Processing of Data Stationary in 1 D or 2 D: Maximum-Entropy Extensions and Time-Varying Relaxations YI Abramovich 1, BA Johnson 2, NK Spencer 3 1 Intelligence,
More informationCompressive Covariance Sensing
EUSIPCO 2014 Lisbon, Portugal Compressive Covariance Sensing A New Flavor of Compressive Sensing Geert Leus Delft University of Technology g.j.t.leus@tudelft.nl Zhi Tian Michigan Technological University
More informationASYMPTOTIC PERFORMANCE ANALYSIS OF DOA ESTIMATION METHOD FOR AN INCOHERENTLY DISTRIBUTED SOURCE. 2πd λ. E[ϱ(θ, t)ϱ (θ,τ)] = γ(θ; µ)δ(θ θ )δ t,τ, (2)
ASYMPTOTIC PERFORMANCE ANALYSIS OF DOA ESTIMATION METHOD FOR AN INCOHERENTLY DISTRIBUTED SOURCE Jooshik Lee and Doo Whan Sang LG Electronics, Inc. Seoul, Korea Jingon Joung School of EECS, KAIST Daejeon,
More informationJ. Liang School of Automation & Information Engineering Xi an University of Technology, China
Progress In Electromagnetics Research C, Vol. 18, 245 255, 211 A NOVEL DIAGONAL LOADING METHOD FOR ROBUST ADAPTIVE BEAMFORMING W. Wang and R. Wu Tianjin Key Lab for Advanced Signal Processing Civil Aviation
More informationResearch Article Joint Phased-MIMO and Nested-Array Beamforming for Increased Degrees-of-Freedom
International Journal of Antennas and Propagation Volume 215, Article ID 989517, 11 pages http://dxdoiorg/11155/215/989517 Research Article Joint Phased-MIMO and Nested-Array Beamforming for Increased
More informationSolving Corrupted Quadratic Equations, Provably
Solving Corrupted Quadratic Equations, Provably Yuejie Chi London Workshop on Sparse Signal Processing September 206 Acknowledgement Joint work with Yuanxin Li (OSU), Huishuai Zhuang (Syracuse) and Yingbin
More informationML ESTIMATION AND CRB FOR NARROWBAND AR SIGNALS ON A SENSOR ARRAY
2014 IEEE International Conference on Acoustic, Speech and Signal Processing ICASSP ML ESTIMATION AND CRB FOR NARROWBAND AR SIGNALS ON A SENSOR ARRAY Langford B White School of Electrical and Electronic
More informationCourse Notes for EE227C (Spring 2018): Convex Optimization and Approximation
Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation Instructor: Moritz Hardt Email: hardt+ee227c@berkeley.edu Graduate Instructor: Max Simchowitz Email: msimchow+ee227c@berkeley.edu
More informationRandom Access Sensor Networks: Field Reconstruction From Incomplete Data
Random Access Sensor Networks: Field Reconstruction From Incomplete Data Fatemeh Fazel Northeastern University Boston, MA Email: fazel@ece.neu.edu Maryam Fazel University of Washington Seattle, WA Email:
More informationEUSIPCO
EUSIPCO 03 56974375 ON THE RESOLUTION PROBABILITY OF CONDITIONAL AND UNCONDITIONAL MAXIMUM LIKELIHOOD DOA ESTIMATION Xavier Mestre, Pascal Vallet, Philippe Loubaton 3, Centre Tecnològic de Telecomunicacions
More informationUsing an Oblique Projection Operator for Highly Correlated Signal Direction-of-Arrival Estimations
Appl. Math. Inf. Sci. 9, No. 5, 2663-2671 (2015) 2663 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.12785/amis/090552 Using an Oblique Projection Operator for
More informationGoing off the grid. Benjamin Recht Department of Computer Sciences University of Wisconsin-Madison
Going off the grid Benjamin Recht Department of Computer Sciences University of Wisconsin-Madison Joint work with Badri Bhaskar Parikshit Shah Gonnguo Tang We live in a continuous world... But we work
More informationOn the Deterministic CRB for DOA Estimation in. Unknown Noise fields Using Sparse Sensor Arrays
On the Deterministic CRB for DOA Estimation in Unknown Noise fields Using Sparse Sensor Arrays Martin Kleinsteuber and Abd-Krim Seghouane National ICT Australia Limited, Canberra Research Laboratory, Locked
More informationSparse Phase Retrieval Using Partial Nested Fourier Samplers
Sparse Phase Retrieval Using Partial Nested Fourier Samplers Heng Qiao Piya Pal University of Maryland qiaoheng@umd.edu November 14, 2015 Heng Qiao, Piya Pal (UMD) Phase Retrieval with PNFS November 14,
More informationCombining Sparsity with Physically-Meaningful Constraints in Sparse Parameter Estimation
UIUC CSL Mar. 24 Combining Sparsity with Physically-Meaningful Constraints in Sparse Parameter Estimation Yuejie Chi Department of ECE and BMI Ohio State University Joint work with Yuxin Chen (Stanford).
More informationRobust multichannel sparse recovery
Robust multichannel sparse recovery Esa Ollila Department of Signal Processing and Acoustics Aalto University, Finland SUPELEC, Feb 4th, 2015 1 Introduction 2 Nonparametric sparse recovery 3 Simulation
More informationAn iterative hard thresholding estimator for low rank matrix recovery
An iterative hard thresholding estimator for low rank matrix recovery Alexandra Carpentier - based on a joint work with Arlene K.Y. Kim Statistical Laboratory, Department of Pure Mathematics and Mathematical
More informationCopyright 2008 IEEE. Reprinted from IEEE Transactions on Signal Processing, 2007; 55 (1):20-31
Copyright 2008 IEEE. Reprinted from IEEE Transactions on Signal Processing, 2007; 55 (1):20-31 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way
More informationLOW COMPLEXITY COVARIANCE-BASED DOA ESTIMATION ALGORITHM
LOW COMPLEXITY COVARIANCE-BASED DOA ESTIMATION ALGORITHM Tadeu N. Ferreira, Sergio L. Netto, and Paulo S. R. Diniz Electrical Engineering Program COPPE/DEL-Poli/Federal University of Rio de Janeiro P.O.
More informationFAST AND ACCURATE DIRECTION-OF-ARRIVAL ESTIMATION FOR A SINGLE SOURCE
Progress In Electromagnetics Research C, Vol. 6, 13 20, 2009 FAST AND ACCURATE DIRECTION-OF-ARRIVAL ESTIMATION FOR A SINGLE SOURCE Y. Wu School of Computer Science and Engineering Wuhan Institute of Technology
More informationA Gain-Phase Error Calibration Method for the Vibration of Wing Conformal Array
Progress In Electromagnetics Research C, Vol 75, 111 119, 2017 A Gain-Phase Error Calibration Method for the Vibration of Wing Conformal Array Wen Hao Du, Wen Tao Li *, and Xiao Wei Shi Abstract Due to
More informationPerformance analysis for time-frequency MUSIC algorithm in presence of both additive noise and array calibration errors
RESEARCH Open Access Performance analysis for time-frequency MUSIC algorithm in presence of both additive noise and array calibration errors Mohamed Khodja *, Adel Belouchrani and Karim Abed-Meraim Abstract
More informationRobust Adaptive Beamforming Based on Low-Complexity Shrinkage-Based Mismatch Estimation
1 Robust Adaptive Beamforming Based on Low-Complexity Shrinkage-Based Mismatch Estimation Hang Ruan and Rodrigo C. de Lamare arxiv:1311.2331v1 [cs.it] 11 Nov 213 Abstract In this work, we propose a low-complexity
More informationVandermonde-form Preserving Matrices And The Generalized Signal Richness Preservation Problem
Vandermonde-form Preserving Matrices And The Generalized Signal Richness Preservation Problem Borching Su Department of Electrical Engineering California Institute of Technology Pasadena, California 91125
More informationSparse DOA estimation with polynomial rooting
Sparse DOA estimation with polynomial rooting Angeliki Xenaki Department of Applied Mathematics and Computer Science Technical University of Denmark 28 Kgs. Lyngby, Denmark Email: anxe@dtu.dk Peter Gerstoft
More informationNear-Field Source Localization Using Sparse Recovery Techniques
Near-Field Source Localization Using Sparse Recovery Techniques Keke Hu, Sundeep Prabhakar Chepuri, and Geert Leus Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University
More informationMassive MIMO: Signal Structure, Efficient Processing, and Open Problems II
Massive MIMO: Signal Structure, Efficient Processing, and Open Problems II Mahdi Barzegar Communications and Information Theory Group (CommIT) Technische Universität Berlin Heisenberg Communications and
More informationarxiv: v4 [cs.it] 21 Dec 2017
Atomic Norm Minimization for Modal Analysis from Random and Compressed Samples Shuang Li, Dehui Yang, Gongguo Tang, and Michael B. Wakin December 5, 7 arxiv:73.938v4 cs.it] Dec 7 Abstract Modal analysis
More informationThe mathematician Ramanujan, and digital signal processing
The mathematician Ramanujan, and digital signal processing P. P. Vaidyanathan California Institute of Technology, Pasadena, CA SAM-2016, Rio, Brazil 1887-1920 Self-educated mathematician from India Grew
More informationANGLE OF ARRIVAL DETECTION USING COMPRESSIVE SENSING
18th European Signal Processing Conference (EUSIPCO-2010) Aalborg, Denmark, August 23-27, 2010 ANGLE OF ARRIVAL DETECTION USING COMPRESSIVE SENSING T. Justin Shaw and George C. Valley The Aerospace Corporation,
More informationGeneralized Sidelobe Canceller and MVDR Power Spectrum Estimation. Bhaskar D Rao University of California, San Diego
Generalized Sidelobe Canceller and MVDR Power Spectrum Estimation Bhaskar D Rao University of California, San Diego Email: brao@ucsd.edu Reference Books 1. Optimum Array Processing, H. L. Van Trees 2.
More informationPOLYNOMIAL SINGULAR VALUES FOR NUMBER OF WIDEBAND SOURCES ESTIMATION AND PRINCIPAL COMPONENT ANALYSIS
POLYNOMIAL SINGULAR VALUES FOR NUMBER OF WIDEBAND SOURCES ESTIMATION AND PRINCIPAL COMPONENT ANALYSIS Russell H. Lambert RF and Advanced Mixed Signal Unit Broadcom Pasadena, CA USA russ@broadcom.com Marcel
More informationSIGNALS with sparse representations can be recovered
IEEE SIGNAL PROCESSING LETTERS, VOL. 22, NO. 9, SEPTEMBER 2015 1497 Cramér Rao Bound for Sparse Signals Fitting the Low-Rank Model with Small Number of Parameters Mahdi Shaghaghi, Student Member, IEEE,
More informationEUSIPCO
EUSIPCO 013 1569746769 SUBSET PURSUIT FOR ANALYSIS DICTIONARY LEARNING Ye Zhang 1,, Haolong Wang 1, Tenglong Yu 1, Wenwu Wang 1 Department of Electronic and Information Engineering, Nanchang University,
More informationMulti-stage convex relaxation approach for low-rank structured PSD matrix recovery
Multi-stage convex relaxation approach for low-rank structured PSD matrix recovery Department of Mathematics & Risk Management Institute National University of Singapore (Based on a joint work with Shujun
More informationExact Joint Sparse Frequency Recovery via Optimization Methods
IEEE TRANSACTIONS ON SIGNAL PROCESSING Exact Joint Sparse Frequency Recovery via Optimization Methods Zai Yang, Member, IEEE, and Lihua Xie, Fellow, IEEE arxiv:45.6585v [cs.it] 3 May 6 Abstract Frequency
More informationDirection of Arrival Estimation Based on Heterogeneous Array
Progress In Electromagnetics Research M, Vol 69, 97 106, 2018 Direction of Arrival Estimation Based on Heterogeneous Array Xiaofei Ren 1, 2, * and Shuxi Gong 1 Abstract Traditionally, the direction of
More informationROBUST ADAPTIVE BEAMFORMING BASED ON CO- VARIANCE MATRIX RECONSTRUCTION FOR LOOK DIRECTION MISMATCH
Progress In Electromagnetics Research Letters, Vol. 25, 37 46, 2011 ROBUST ADAPTIVE BEAMFORMING BASED ON CO- VARIANCE MATRIX RECONSTRUCTION FOR LOOK DIRECTION MISMATCH R. Mallipeddi 1, J. P. Lie 2, S.
More informationOn the recovery of measures without separation conditions
Norbert Wiener Center Department of Mathematics University of Maryland, College Park http://www.norbertwiener.umd.edu Applied and Computational Mathematics Seminar Georgia Institute of Technology October
More informationCP DECOMPOSITION AND ITS APPLICATION IN NOISE REDUCTION AND MULTIPLE SOURCES IDENTIFICATION
International Conference on Computer Science and Intelligent Communication (CSIC ) CP DECOMPOSITION AND ITS APPLICATION IN NOISE REDUCTION AND MULTIPLE SOURCES IDENTIFICATION Xuefeng LIU, Yuping FENG,
More informationCovariance smoothing and consistent Wiener filtering for artifact reduction in audio source separation
Covariance smoothing and consistent Wiener filtering for artifact reduction in audio source separation Emmanuel Vincent METISS Team Inria Rennes - Bretagne Atlantique E. Vincent (Inria) Artifact reduction
More informationcomputation of the algorithms it is useful to introduce some sort of mapping that reduces the dimension of the data set before applying signal process
Optimal Dimension Reduction for Array Processing { Generalized Soren Anderson y and Arye Nehorai Department of Electrical Engineering Yale University New Haven, CT 06520 EDICS Category: 3.6, 3.8. Abstract
More information20th European Signal Processing Conference (EUSIPCO 2012) Bucharest, Romania, August 27-31, 2012
2th European Signal Processing Conference (EUSIPCO 212) Bucharest, Romania, August 27-31, 212 A NEW TOOL FOR MULTIDIMENSIONAL LOW-RANK STAP FILTER: CROSS HOSVDS Maxime Boizard 12, Guillaume Ginolhac 1,
More information2-D SENSOR POSITION PERTURBATION ANALYSIS: EQUIVALENCE TO AWGN ON ARRAY OUTPUTS. Volkan Cevher, James H. McClellan
2-D SENSOR POSITION PERTURBATION ANALYSIS: EQUIVALENCE TO AWGN ON ARRAY OUTPUTS Volkan Cevher, James H McClellan Georgia Institute of Technology Atlanta, GA 30332-0250 cevher@ieeeorg, jimmcclellan@ecegatechedu
More informationSFT-BASED DOA DETECTION ON CO-PRIME ARRAY
SFT-BASED DOA DETECTION ON CO-PRIME ARRAY BY GUANJIE HUANG A thesis submitted to the Graduate School New Brunswick Rutgers, The State University of New Jersey in partial fulfillment of the requirements
More informationMultiple Antennas. Mats Bengtsson, Björn Ottersten. Channel characterization and modeling 1 September 8, Signal KTH Research Focus
Multiple Antennas Channel Characterization and Modeling Mats Bengtsson, Björn Ottersten Channel characterization and modeling 1 September 8, 2005 Signal Processing @ KTH Research Focus Channel modeling
More information