Adaptive Array Detection, Estimation and Beamforming
|
|
- Felicity Bell
- 5 years ago
- Views:
Transcription
1 Adaptive Array Detection, Estimation and Beamforming Christ D. Richmond Workshop on Stochastic Eigen-Analysis and its Applications 3:30pm, Monday, July 10th 2006 C. D. Richmond-1 *This work was sponsored by Defense Advanced Research Projects Agency under Air Force contract FA C Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government.
2 Outline Introduction Radar/Sonar problem Detection algorithms Estimation algorithms Open problems Summary C. D. Richmond-2
3 Airborne Surveillance Radars RAdio Detection And Ranging = RADAR Goals / Mission: Long range surveillance Airborne Moving Target Indication (AMTI) Ground Moving Target Indication (GMTI) Synthetic Aperture Radar (SAR) Imaging C. D. Richmond-3
4 Airborne Surveillance Radars: Signals and Interference Hostile Jamming Interferer Azimuth Target s TX TX/RX Waveform { } ( t)= Re p ( t) e j 2πf c t Ground Clutter s RX { } ()= t Re α p ( t τ) e j 2π ( f c + f d )t Transmit Power Pattern v Time Delay (Range) Doppler (Velocity) C. D. Richmond-4
5 Radar Data Model and Optimum Linear Filter Primary snapshot (target range gate) x T = Sv( T, f T )+ n C. D. Richmond-5 Ground Clutter ( ) E{ x T }= Sv T, f T cov( x T )= E{ nn H }= R *Brennan and Reed, IEEE T-AES 1973 First to propose this for Radar Sig. Proc Power (db) Clutter Null NOISE Sin (Azimuth) Filter Response of w R 1 v 0 CLUTTER TARGET Two-dimensional filtering required to cancel ground clutter Space-Time Adaptive Processing (STAP) 1 Jammer Null JAMMER 0 Doppler (Hz) 0.5
6 Outline Introduction Detection algorithms Estimation algorithms Open problems Summary C. D. Richmond-6
7 Adaptive Detection Problem Test Cell: Analogy to 1-D H 0 : x T = n cov(x T ) = R CFAR Statistic H 1 : x T = Sv T + n Two Unknowns R & S Use Noise Only Training Set [ x x ] X = 1 2 x L cov(x i ) = R Assumptions: All Data Complex Gaussian Training Samples Homogeneous with Test Cell cov(x T ) = cov(x i ) Perfect Look ( v = v T ) t = #Cells ˆ 2 2 η σ N < C. D. Richmond-7
8 Summary of Adaptive Detection Algorithms Adaptive Matched Filter (AMF) Robey, et. al. IEEE T-AES 1992 Reed & Chen 1992, Reed et. al Generalized Likelihood Ratio Test (GLRT) Kelly IEEE T-AES 1986, Khatri 1979 Adaptive Cosine Estimator (ACE) Conte et. al. IEEE T-AES 1995, Scharf et. al. Asilomar 1996 Adaptive Sidelobe Blanker (ASB) Kreithen, Baranoski, 1996 Richmond Asilomar 1997 More t AMF = vh ˆ R 1 x T 2 v H ˆ R 1 v t GLRT = t ACE = t AMF H ˆ 1 L + x T f (t AMF,t ACE ) R 1 x T t AMF x T H ˆ R 1 x T C. D. Richmond-8 Each Algorithm is a function of the Sample Covariance ˆ R = 1 L x 1x 1 H + x 2 x 2 H + + x L x L H ( )
9 1 0.8 R known PD Adaptive Detection Performance: An Example PD vs SINR Loss Due to Covariance Estimation GLRT ASB, Fixed Thr. ACE AMF Max ASB PD Optimal MF N=10, L=2N, PFA=1e R unknown Output Array SINR (db) Random matrix theory predicts performance loss due to covariance estimation C. D. Richmond-9
10 Outline Introduction Detection algorithms Estimation algorithms Open problems Summary C. D. Richmond-10
11 Mean-Squared Error Performance: No Mismatch vs Mismatch No Mismatch Array Element Positions ˆ ML = argmax Ambiguity Function t ML (,data) Noise Free Mean Squared Error (db) No Information Threshold T Cramr-Rao Bound Driven by Global Ambiguity/Sidelobe Errors Asymptotic Driven by Local Mainlobe Errors Large Errors ˆ ML Small Errors T T Scan Angle Low SNR High SNR SNR TH SNR (db) T ˆ ML C. D. Richmond-11
12 Mean-Squared Error Performance: No Mismatch vs Mismatch No Mismatch Array Element Positions Assumed True Signal Mismatch Array Element Positions T T Sidelobe Target Mean Squared Error (db) No Information Threshold Cramr-Rao Bound Mismatch affects threshold and asymptotic region leading to atypical performance curves Asymptotic Mean Squared Error (db) No Information CRB Threshold Asymptotic C. D. Richmond-12 SNR SNR (db) TH SNR (db) SNR TH SNR TH Mismatch
13 Data Model: ML Estimator*: Data Model: ML Estimator*: Maximum-Likelihood Signal Parameter Estimation π N R 1 exp x Sv [ ()] H R 1 x Sv() [ ] { } ML = argmaxt MF ( ) t MF ()= vh ()R 1 x 2 v H ()R 1 v() π N(L +1) R (L +1) exp x Sv ML = argmaxt AMF ( ) t AMF {[ ()] H R 1 [ x Sv() ] tr( R 1 XX H )} ()ˆ ()= vh R 1 x 2 R ˆ 1 v H ()ˆ R 1 v() L XX H Test Cell Training Data Complex Gaussian data model: All snapshots N x 1 Arbitrary N x N Colored Covariance Deterministic Signal ( Conditional ) Colored noise only training samples available S unknown Clairvoyant Matched Filter R unknown S unknown Adaptive Matched Filter *See Swindlehurst & Stoica Proc. IEEE 1998 C. D. Richmond-13
14 Approximating MSE Performance: Based on Interval Errors MSE given by E ˆ 1 ( ) 2 1 E ˆ ML 1 ( ) 2 ω 1 ( ) 2 p ˆ ()dω ω ( ω 1 ) 2 dω 1 K k= 2 p ˆ ML = k 1 IE Local Errors ( ) NIE IE Global Errors K 2 σ ML ( 1 )+ p( ˆ ML = k ) 1 k 1 k= 2 ( ) 2 Challenge is calculation of error probabilities p( ˆ ML = k ) =? 1 and asymptotic MSE: 2 σ ML ( )=? 1 Both are functions of the estimated covariance C. D. Richmond-14
15 Broadside Planewave Signal in White Noise: No Mismatch, R known, ULA ULA Element Positions z n Distance (in units of λ) RMSE in Beamwidths (db) From 4000 Monte Carlo Simulations Threshold SNR Var. Uniform CRB Asympt. MSE MSE Prediction Monte Carlo Element Level SNR (db) N=18 element uniform linear array (ULA), (λ/2.25) element spacing 3dB Beamwidth 7.2 degs, search space [60 120] degs 0dB white noise, True 90 degs (broadside) Asymptotic ML MSE agrees with CRB above threshold SNR MIE MSE predictions very accurate above and below threshold C. D. Richmond-15
16 ULA Element Positions 2 ( 0 I ) z n + N σ 3, 3 RMS σ RMS = 0. 1λ Signal in White Noise: Perturbed ULA, R unknown, L = 3N Distance (in units of λ) RMSE in Beamwidths (db) From 4000 Monte Carlo Simulations Element Level SNR (db) Asympt. MSE MSE Prediction CRB Threshold SNR N=18 element ULA positions perturbed by 3-D Gaussian noise Zero mean with stand. dev. 0.1λ; use single realization MC Known R MC Unknown R Adaptivity Loss Estimated colored noise covariance from L = 3N samples ~15dB SNR adaptivity loss limits beam split ratio to 16:1 as opposed to 22:1 when R is known C. D. Richmond-16
17 The Capon-MVDR Algorithm T Capon proposed filterbank approach to spectral estimation that designs linear filters optimally: Given N x 1 vector snapshots for l =1,2,,L ( ) x l with covariance choose filter weights w according to min w H Rw such that w H v( )=1 Minimum Variance { ( )} R = E x( l)x H l Distortionless Response Capon 1969 Solution well-known: R 1 v E w H v { ()x () l 2 } = 1 H w()= C. D. Richmond-17 () ()R 1 v() where ˆ R = 1 L L l=1 Average Output Power of Optimal Filter: x()x l H () l v H ()R 1 v() Capon s Spectrum: P Capon () is sample covariance matrix Parameter estimate ˆ given by location of maximum power 1 Ambiguity Estimation Function v H ()R 1 v () Error T Scan Angle ˆ T v H v H 1 ()ˆ R 1 v() 1 R 1 v ()ˆ () Scan Angle
18 Diagonally Loaded Capon Algorithm In practice it is common to diagonally load the sample covariance: ˆ R α = α I + 1 L L l=1 x()x l H () l I P Capon (,α )= v H 1 R 1 α v ()ˆ () * Robustify Processing Diagonal loading mitigates undesired finite sample effects* Slow convergence of small/noise eigenvalues (DL compresses) High sidelobes (DL provides sidelobe [white noise gain] control) Excessive loading can degrade performance Diagonal loading is necessary to invert matrix in snapshot deficient aacase, i.e. L N Eigenvectors of sample covariance remain unaffected by diagonal aaloading Featherstone et al. showed diagonally loaded Capon to be a robust aadirection finding algorithm C. D. Richmond-18 *Cox, IEEE T-SP 1987, Carlson, IEEE T-AES 1988
19 Single Signal Broadside to Array in Spatially White Noise, L = 0.5N RMSE in Beamwidths (db) Monte Carlo CRB MSE Prediction L = 0.5N α = -10dB RMSE in Beamwidths (db) Threshold SNRs L = 0.5N α = +10dB Output Array SNR (db) Output Array SNR (db) C. D. Richmond-19 N=18 element uniform linear array (ULA), (λ/2.25) element spacing 3dB Beamwidth 7.2 degs 0dB white noise, True 90 degs (broadside) 4000 Monte Carlo simulations VB MSE prediction not applicable for L < N
20 Mismatch Example: Perturbed Array Positions Assumed Nominal Array Position Actual Perturbed Array Position z n z n + e n RMSE in Beamwidths (db) Monte Carlo MSE Prediction VB MSE Prediction L = 1.5N α = +10dB 8dB Error in VB Prediction of 15:1 Beamsplit Ratio SNR 10dB Error for 17:1 Based on Single Realization of Gaussian Perturbation: 2 e n ~ N 3 ( 0,I 3 σ RMS ), C. D. Richmond-20 σ RMS = 0.04λ Output Array SNR (db) N=18 element ULA with perturbed positions but assumed straight VB MSE prediction can lead to large errors in required SNRs DL Capon is more robust DF approach: 18:1 vs 40dB ASNR
21 Outline Introduction Detection algorithms Estimation algorithms Summary Open problems C. D. Richmond-21
22 What About Robust Detection? Adaptive Matched Filter (AMF) Robey, et. al. IEEE T-AES 1992 Reed & Chen 1992, Reed et. al Generalized Likelihood Ratio Test (GLRT) Kelly IEEE T-AES 1986, Khatri 1979 Adaptive Cosine Estimator (ACE) Conte et. al. IEEE T-AES 1995, Scharf et. al. Asilomar 1996 Adaptive Sidelobe Blanker (ASB) Kreithen, Baranoski, 1996 Richmond Asilomar 1997 C. D. Richmond-22 Each Algorithm is a function of the Sample Covariance t AMF α t GLRT t ACE ˆ R α = α I + 1 L ()= vh ˆ ( α)= ( α)= R 1 α x T 2 v H R ˆ 1 α v t AMF 1 + x T H ˆ R α 1 x T t AMF x T H ˆ R α 1 x T [ ( )] f t AMF ( α ), t ACE α L l=1 x()x l H () l
23 Magneto-encephalography (MEG) z (meters) False Peaks Source True Location P LCMV ( ) (db) y (meters) Inflated Cortical Surface LCMV Cost Function - Based on 74 Channel Dual Sensor Magnes II Biomagnetometer - SNR = -23 db Dipolar source located in the center of the Somatosensory Region C. D. Richmond-23 x (meters)
24 Composite Localization Accuracy vs Signal-to-Noise Ratio (SNR) Localization MSE (db) No Information: No Signal Threshold: Weak Signal -log(snr) Large Errors Due To False Peaks of Cost Function Asymptotic: Strong Signal Cost Function: Output of LCMV spatial filter as signal location hypothesis is varied when using true R Residual Error Due to Jitter About True Source Location Cost Fnc Height High Outstanding Problem THRESHOLD SNR H Pr tr V ˆ 1 R 1 V 1 ( ) 1 SNR (db) >tr ( V H ˆ 2 R 1 V ) 1 2 =? Low C. D. Richmond-24
25 Outline Introduction Detection algorithms Estimation algorithms Open problems Summary C. D. Richmond-25
26 Summary Random matrix theory provides insight into the performance of adaptive arrays systems Finite random matrix theory has been most common approach Infinite random matrix theory quickly gaining momentum as tool for analyses and design of robust signal processing algorithms C. D. Richmond-26
27 Distributions of 1-D Detectors Homogeneous Case Recall that PD of ASB is PD ASB = Pr( t ACE > η ace, t AMF > η amf ) Requires knowledge of Dependence! ~ t Define the following GLRT t GLRT /(1 t GLRT ) ~ t ACE t ACE /(1 t ACE ) K = L N + 2 It can be shown that Found in this Summary! Distributions of Adaptive Detectors t GLRT t AMF = d F 1,K 1 ( δ β ) = d F 1,K 1 ( δ β )/β where δ β = β S v H R -1 v t ACE = d F 1,K 1 ( δ β )/(1 β ) Richmond Asilomar 1997 Richmond IEEE SP 2000 C. D. Richmond-27
28 The AMF Detector Form the optimal Neyman-Pearson test statistic, that is, the LRT. Assume complex Gaussian statistics H 0 : H 1 : g H 0 = π N R 1 exp[ x H T R 1 x T ] g H1 = π N R T 1exp x T vs [ ( ) H R 1 ( x T vs) ] Likelihood = RatioTest max S g H 0 g H1 = v H ˆ R 1 x T 2 v H ˆ R 1 v t MF Matched Filter Simply replace true data covariance with an estimate XX H = ˆ R R t AMF = v H v Rˆ H 1 Rˆ x 1 T v 2 Known as the Adaptive Matched Filter (AMF) detector C. D. Richmond-28 Return
29 The Generalized LRT (GLRT) Form the LRT based on the totality of data: Assume homogeneous complex gaussian statistics where [ Test Cell Interference Training Set ]= [ x T X] X 0 H 0 : H 1 : M = [ vs 0] g H 0 [ ] = π N(L +1) R (L +1) exp trr 1 X 0 X 0 H [ ( )( X 0 M) H ] g H 1 = π N ( L +1) R ( L +1) exp trr 1 X 0 M Maximize likelihood functions over all unknown parameters: t GLRT = max S,R max R g H1 g H 0 1 L +1 = H 1+ x ˆ T R 1 x T ˆ H 1+ x ˆ T R 1 x T vh R 1 2 x T v H R ˆ 1 v C. D. Richmond-29 Known as Kelly s / Khatri s GLRT Return
30 The Adaptive Cosine Estimator (ACE) ψ v Target array response Measured data vector x The ACE statistic provides a measure of correlation between the test data vector x T and the assumed target array response v Inner product space defined wrt inverse of data covariance in whitened space t ACE = v H ˆ R 1 x T 2 ( H x ˆ T R 1 x T )v H R ˆ 1 v ( ) = cosψ 2 C. D. Richmond-30 Return
31 Simplest: The AMF Detector Practical Issues: AMF Computationally Attractive: Linear Filter AMF is an Adaptive Beamformer Measures Power in Assumed Target Direction Interference Suppression Based on Covariance Estimate Inhomogeneities Frustrate Interference Suppression Covariance Estimate Uncharacteristic of Data Results in high False Alarm Rates C. D. Richmond-31
32 Classical Sidelobe Blanking Directional Channel Threshold < Power in Target Direction Gate Output Input Omni-directional Channel Threshold < Comparator Total Power from All Directions Channel Magnitude Response Typical Comparator Input Ch 2 Ch 1 Ch 1 Ch 2 Strong Signal Time Azimuth C. D. Richmond-32
33 2-D ASB Detection Algorithm Step 1: Beamforming t AMF Power in Target Direction > η amf Step 2 : Sidelobe Blanking t AMF > η ace x T H ˆ R -1 x T Power in Target Direction Total Power From All Directions Sidelobe Blanking t ACE 1 0 η ace 2-D ASB Detector Passes ACE Fails AMF & ACE η amf Region of Declared Detections Passes AMF Directional Beamformer t AMF Return C. D. Richmond-33
34 The Complex Wishart Random Matrix If the training data is complex Gaussian s.t. X ~ CN( 0,I L R) then the sample covariance matrix is the Maximum-Likelihood estimator of the covariance parameter R: L N L ˆ R XX H = L k=1 x k x k H If then its PDF exists and is given by LR ˆ L N R L / Γ N (L) [ ( )] where 0 < ˆ exp tr R 1ˆ R L and the differential volume element is given by ( dr ˆ )= dr ˆ 11 dr ˆ 22 dr ˆ NN d Re( R ˆ 12 )d Im( R ˆ 12 ) d Re( R ˆ 13 )d Im ˆ d Re( R ˆ N 1,N )d Im( R ˆ N 1,N ) R ( R ) 13 C. D. Richmond-34
The Probability Distribution of the MVDR Beamformer Outputs under Diagonal Loading. N. Raj Rao (Dept. of Electrical Engineering and Computer Science)
The Probability Distribution of the MVDR Beamformer Outputs under Diagonal Loading N. Raj Rao (Dept. of Electrical Engineering and Computer Science) & Alan Edelman (Dept. of Mathematics) Work supported
More informationA GLRT FOR RADAR DETECTION IN THE PRESENCE OF COMPOUND-GAUSSIAN CLUTTER AND ADDITIVE WHITE GAUSSIAN NOISE. James H. Michels. Bin Liu, Biao Chen
A GLRT FOR RADAR DETECTION IN THE PRESENCE OF COMPOUND-GAUSSIAN CLUTTER AND ADDITIVE WHITE GAUSSIAN NOISE Bin Liu, Biao Chen Syracuse University Dept of EECS, Syracuse, NY 3244 email : biliu{bichen}@ecs.syr.edu
More informationPerformance Analysis of the Nonhomogeneity Detector for STAP Applications
Performance Analysis of the Nonhomogeneity Detector for STAP Applications uralidhar Rangaswamy ARCON Corporation 6 Bear ill Rd. Waltham, assachusetts, USA Braham imed AFRL/SNRT 6 Electronic Parway Rome,
More informationSignal Processing for MIMO Radars. under Gaussian and non-gaussian environments and application to STAP
Signal processing for MIMO radars: Detection under Gaussian and non-gaussian environments and application to STAP CHONG Chin Yuan Thesis Director: Marc LESTURGIE (ONERA/SONDRA) Supervisor: Frédéric PASCAL
More informationPassive Sonar Detection Performance Prediction of a Moving Source in an Uncertain Environment
Acoustical Society of America Meeting Fall 2005 Passive Sonar Detection Performance Prediction of a Moving Source in an Uncertain Environment Vivek Varadarajan and Jeffrey Krolik Duke University Department
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 informationADAPTIVE ARRAY DETECTION ALGORITHMS WITH STEERING VECTOR MISMATCH
ADAPTIVE ARRAY DETECTIO ALGORITHMS WITH STEERIG VECTOR MISMATCH LIM Chin Heng, Elias Aboutanios Bernard Mulgrew Institute for Digital Communications School of Engineering & Electronics, University of Edinburgh
More informationCHAPTER 3 ROBUST ADAPTIVE BEAMFORMING
50 CHAPTER 3 ROBUST ADAPTIVE BEAMFORMING 3.1 INTRODUCTION Adaptive beamforming is used for enhancing a desired signal while suppressing noise and interference at the output of an array of sensors. It is
More informationarxiv: v1 [cs.it] 6 Nov 2016
UNIT CIRCLE MVDR BEAMFORMER Saurav R. Tuladhar, John R. Buck University of Massachusetts Dartmouth Electrical and Computer Engineering Department North Dartmouth, Massachusetts, USA arxiv:6.272v [cs.it]
More informationBEAMFORMING DETECTORS WITH SUBSPACE SIDE INFORMATION. Andrew Bolstad, Barry Van Veen, Rob Nowak
BEAMFORMING DETECTORS WITH SUBSPACE SIDE INFORMATION Andrew Bolstad, Barry Van Veen, Rob Nowak University of Wisconsin - Madison 45 Engineering Drive Madison, WI 5376-69 akbolstad@wisc.edu, vanveen@engr.wisc.edu,
More informationBeamspace Adaptive Channel Compensation for Sensor Arrays with Faulty Elements
1 2005 Asilomar Conference Beamspace Adaptive Channel Compensation for Sensor Arrays with Faulty Elements Oguz R. Kazanci and Jeffrey L. Krolik Duke University Department of Electrical and Computer Engineering
More informationMaximum Likelihood Methods in Radar Array Signal Processing
Maximum Likelihood Methods in Radar Array Signal Processing A. LEE SWINDLEHURST, MEMBER, IEEE, AND PETRE STOICA, FELLOW, IEEE We consider robust and computationally efficient maximum likelihood algorithms
More informationAntonio De Maio, Maria S. Greco, and Danilo Orlando. 1.1 Historical Background and Terminology Symbols Detection Theory 6
Contents 1 Introduction to Radar Detection 1 Antonio De Maio, Maria S. Greco, and Danilo Orlando 1.1 Historical Background and Terminology 1 1.2 Symbols 5 1.3 Detection Theory 6 1.3.1 Signal and Interference
More informationRobust Space-Time Adaptive Processing Using Projection Statistics
Robust Space-Time Adaptive Processing Using Projection Statistics André P. des Rosiers 1, Gregory N. Schoenig 2, Lamine Mili 3 1: Adaptive Processing Section, Radar Division United States Naval Research
More informationOverview of Beamforming
Overview of Beamforming Arye Nehorai Preston M. Green Department of Electrical and Systems Engineering Washington University in St. Louis March 14, 2012 CSSIP Lab 1 Outline Introduction Spatial and temporal
More informationRobust Capon Beamforming
Robust Capon Beamforming Yi Jiang Petre Stoica Zhisong Wang Jian Li University of Florida Uppsala University University of Florida University of Florida March 11, 2003 ASAP Workshop 2003 1 Outline Standard
More informationApplications of Robust Optimization in Signal Processing: Beamforming and Power Control Fall 2012
Applications of Robust Optimization in Signal Processing: Beamforg and Power Control Fall 2012 Instructor: Farid Alizadeh Scribe: Shunqiao Sun 12/09/2012 1 Overview In this presentation, we study the applications
More informationBeamforming Arrays with Faulty Sensors in Dynamic Environments
Beamforming Arrays with Faulty Sensors in Dynamic Environments Jeffrey Krolik and Oguz R. Kazanci Duke University phone: 919-660-5274 email: jk@ee.duke.edu Abstract This paper addresses the problem of
More informationMULTIPLE-CHANNEL DETECTION IN ACTIVE SENSING. Kaitlyn Beaudet and Douglas Cochran
MULTIPLE-CHANNEL DETECTION IN ACTIVE SENSING Kaitlyn Beaudet and Douglas Cochran School of Electrical, Computer and Energy Engineering Arizona State University, Tempe AZ 85287-576 USA ABSTRACT The problem
More informationVirtual Array Processing for Active Radar and Sonar Sensing
SCHARF AND PEZESHKI: VIRTUAL ARRAY PROCESSING FOR ACTIVE SENSING Virtual Array Processing for Active Radar and Sonar Sensing Louis L. Scharf and Ali Pezeshki Abstract In this paper, we describe how an
More informationKnowledge-Aided STAP Processing for Ground Moving Target Indication Radar Using Multilook Data
Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 6, Article ID 74838, Pages 1 16 DOI 1.1155/ASP/6/74838 Knowledge-Aided STAP Processing for Ground Moving Target Indication
More informationRobust Range-rate Estimation of Passive Narrowband Sources in Shallow Water
Robust Range-rate Estimation of Passive Narrowband Sources in Shallow Water p. 1/23 Robust Range-rate Estimation of Passive Narrowband Sources in Shallow Water Hailiang Tao and Jeffrey Krolik Department
More informationDETECTION PERFORMANCE FOR THE GMF APPLIED TO STAP DATA
4th European Signal Processing Conference (EUSIPCO 26), Florence, Italy, September 4-8, 26, copyright by EURASIP DETECTION PERFORMANCE FOR THE GMF APPLIED TO STAP DATA Sébastien MARIA, Jean-Jacques Fuchs
More informationCognitive MIMO Radar
Cognitive MIMO Radar Joseph Tabriian Signal Processing Laboratory Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Involved collaborators and Research Assistants: Prof.
More informationEXTENDED GLRT DETECTORS OF CORRELATION AND SPHERICITY: THE UNDERSAMPLED REGIME. Xavier Mestre 1, Pascal Vallet 2
EXTENDED GLRT DETECTORS OF CORRELATION AND SPHERICITY: THE UNDERSAMPLED REGIME Xavier Mestre, Pascal Vallet 2 Centre Tecnològic de Telecomunicacions de Catalunya, Castelldefels, Barcelona (Spain) 2 Institut
More informationAnalysis of Optimal Diagonal Loading for MPDR-based Spatial Power Estimators in the Snapshot Deficient Regime
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Analysis of Optimal Diagonal Loading for MPDR-based Spatial Power Estimators in the Snapshot Deficient Regime Pajovic, M.; Preisig, J.C.; Baggeroer,
More informationOptimal Time Division Multiplexing Schemes for DOA Estimation of a Moving Target Using a Colocated MIMO Radar
Optimal Division Multiplexing Schemes for DOA Estimation of a Moving Target Using a Colocated MIMO Radar Kilian Rambach, Markus Vogel and Bin Yang Institute of Signal Processing and System Theory University
More informationResearch Article Robust STAP for MIMO Radar Based on Direct Data Domain Approach
Hindawi International Journal of Antennas and Propagation Volume 217, Article ID 696713, 9 pages https://doi.org/1.1155/217/696713 Research Article Robust STAP for MIMO Radar Based on Direct Data Domain
More informationFinite Sampling Considerations for GMTI STAP and Sensor Modeling
MTR 03B0000075 MITRE TECHNICAL REPORT Finite Sampling Considerations for GMTI STAP and Sensor Modeling September 2003 T. P. Guella B. N. Suresh Babu Sponsor: ESC Contract No.: F19628-99-C-0001 Dept. No.:
More informationRobust covariance matrices estimation and applications in signal processing
Robust covariance matrices estimation and applications in signal processing F. Pascal SONDRA/Supelec GDR ISIS Journée Estimation et traitement statistique en grande dimension May 16 th, 2013 FP (SONDRA/Supelec)
More informationSensitivity Considerations in Compressed Sensing
Sensitivity Considerations in Compressed Sensing Louis L. Scharf, 1 Edwin K. P. Chong, 1,2 Ali Pezeshki, 2 and J. Rockey Luo 2 1 Department of Mathematics, Colorado State University Fort Collins, CO 8523,
More informationNear Optimal Adaptive Robust Beamforming
Near Optimal Adaptive Robust Beamforming The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation
More informationWHEN IS A MAXIMAL INVARIANT HYPOTHESIS TEST BETTER THAN THE GLRT? Hyung Soo Kim and Alfred O. Hero
WHEN IS A MAXIMAL INVARIANT HYPTHESIS TEST BETTER THAN THE GLRT? Hyung Soo Kim and Alfred. Hero Department of Electrical Engineering and Computer Science University of Michigan, Ann Arbor, MI 489-222 ABSTRACT
More informationCFAR TARGET DETECTION IN TREE SCATTERING INTERFERENCE
CFAR TARGET DETECTION IN TREE SCATTERING INTERFERENCE Anshul Sharma and Randolph L. Moses Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210 ABSTRACT We have developed
More informationDETECTION theory deals primarily with techniques for
ADVANCED SIGNAL PROCESSING SE Optimum Detection of Deterministic and Random Signals Stefan Tertinek Graz University of Technology turtle@sbox.tugraz.at Abstract This paper introduces various methods for
More informationIterative Algorithms for Radar Signal Processing
Iterative Algorithms for Radar Signal Processing Dib Samira*, Barkat Mourad**, Grimes Morad*, Ghemit Amal* and amel Sara* *Department of electronics engineering, University of Jijel, Algeria **Department
More informationKNOWLEDGE-BASED STAP FOR AIRBORNE RADAR
KNOWLEDGE-BASED STAP FOR AIRBORNE RADAR Michael C. Wicks 1, Muralidhar Rangaswamy 2, Raviraj Adve 3, and Todd B. Hale 4 1 AFRL/SN, Rome, NY (Michael.Wicks@rl.af.mil) 2 AFRL/SNHE Hanscom AFB, MA (Muralidhar.Rangaswamy@hanscom.af.mil)
More informationIntrinsic Estimation Bounds with Signal Processing Applications
Intrinsic Estimation Bounds with Signal Processing Applications Steven T. Smith *, Lexington, MA 02420; stsmith@ll.mit.edu. This work was sponsored by DARPA under Air Force contract FA8721-05-C-0002. Opinions,
More informationTarget Detection using Weather Radars and Electromagnetic Vector Sensors
Target Detection using Weather Radars and Electromagnetic Vector Sensors Prateek Gundannavar and Arye Nehorai Email: nehorai@ese.wustl.edu Preston M. Green Department of Electrical & Systems Engineering
More informationCompressed Statistical Testing and Application to Radar
Compressed Statistical Testing and Application to Radar Hsieh-Chung Chen, H. T. Kung, and Michael C. Wicks Harvard University, Cambridge, MA, USA University of Dayton, Dayton, OH, USA Abstract We present
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 informationELEG 5633 Detection and Estimation Signal Detection: Deterministic Signals
ELEG 5633 Detection and Estimation Signal Detection: Deterministic Signals Jingxian Wu Department of Electrical Engineering University of Arkansas Outline Matched Filter Generalized Matched Filter Signal
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 informationLecture 5: Likelihood ratio tests, Neyman-Pearson detectors, ROC curves, and sufficient statistics. 1 Executive summary
ECE 830 Spring 207 Instructor: R. Willett Lecture 5: Likelihood ratio tests, Neyman-Pearson detectors, ROC curves, and sufficient statistics Executive summary In the last lecture we saw that the likelihood
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 informationLecture 8: Signal Detection and Noise Assumption
ECE 830 Fall 0 Statistical Signal Processing instructor: R. Nowak Lecture 8: Signal Detection and Noise Assumption Signal Detection : X = W H : X = S + W where W N(0, σ I n n and S = [s, s,..., s n ] T
More informationBeamspace Adaptive Beamforming and the GSC
April 27, 2011 Overview The MVDR beamformer: performance and behavior. Generalized Sidelobe Canceller reformulation. Implementation of the GSC. Beamspace interpretation of the GSC. Reduced complexity of
More informationDetection theory 101 ELEC-E5410 Signal Processing for Communications
Detection theory 101 ELEC-E5410 Signal Processing for Communications Binary hypothesis testing Null hypothesis H 0 : e.g. noise only Alternative hypothesis H 1 : signal + noise p(x;h 0 ) γ p(x;h 1 ) Trade-off
More informationA Bound on Mean-Square Estimation Error Accounting for System Model Mismatch
A Bound on Mean-Square Estimation Error Accounting for System Model Mismatch Wen Xu RD Instruments phone: 858-689-8682 email: wxu@rdinstruments.com Christ D. Richmond MIT Lincoln Laboratory email: christ@ll.mit.edu
More informationSPACE-TIME ADAPTIVE PROCESSING BASED ON WEIGHTED REGULARIZED SPARSE RECOVERY
Progress In Electromagnetics Research B, Vol. 42, 245 262, 212 SPACE-TIME ADAPTIVE PROCESSING BASED ON WEIGHTED REGULARIZED SPARSE RECOVERY Z. C. Yang *, X. Li, and H. Q. Wang Electronics Science and Engineering
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 informationNovel spectrum sensing schemes for Cognitive Radio Networks
Novel spectrum sensing schemes for Cognitive Radio Networks Cantabria University Santander, May, 2015 Supélec, SCEE Rennes, France 1 The Advanced Signal Processing Group http://gtas.unican.es The Advanced
More informationSignal Detection Basics - CFAR
Signal Detection Basics - CFAR Types of noise clutter and signals targets Signal separation by comparison threshold detection Signal Statistics - Parameter estimation Threshold determination based on the
More informationA SIRV-CFAR Adaptive Detector Exploiting Persymmetric Clutter Covariance Structure
A SIRV-CFAR Adaptive Detector Exploiting Persymmetric Clutter Covariance Structure Guilhem Pailloux 2 Jean-Philippe Ovarlez Frédéric Pascal 3 and Philippe Forster 2 ONERA - DEMR/TSI Chemin de la Hunière
More informationReduced-dimension space-time adaptive processing based on angle-doppler correlation coefficient
Li et al. EURASIP Journal on Advances in Signal Processing 2016 2016:97 DOI 10.1186/s13634-016-0395-2 EURASIP Journal on Advances in Signal Processing RESEARCH Open Access Reduced-dimension space-time
More informationKNOWLEDGE-AIDED SIGNAL PROCESSING
KNOWLEDGE-AIDED SIGNAL PROCESSING By XUMIN ZHU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
More informationAn Adaptive Beamformer Based on Adaptive Covariance Estimator
Progress In Electromagnetics Research M, Vol. 36, 149 160, 2014 An Adaptive Beamformer Based on Adaptive Covariance Estimator Lay Teen Ong * Abstract Based on the Minimum Variance Distortionless Response-Sample
More informationVariations. ECE 6540, Lecture 10 Maximum Likelihood Estimation
Variations ECE 6540, Lecture 10 Last Time BLUE (Best Linear Unbiased Estimator) Formulation Advantages Disadvantages 2 The BLUE A simplification Assume the estimator is a linear system For a single parameter
More informationDetection in reverberation using space time adaptive prewhiteners
Detection in reverberation using space time adaptive prewhiteners Wei Li,,2 Xiaochuan Ma, Yun Zhu, Jun Yang,,2 and Chaohuan Hou Institute of Acoustics, Chinese Academy of Sciences 2 Graduate University
More informationUncertainty. Jayakrishnan Unnikrishnan. CSL June PhD Defense ECE Department
Decision-Making under Statistical Uncertainty Jayakrishnan Unnikrishnan PhD Defense ECE Department University of Illinois at Urbana-Champaign CSL 141 12 June 2010 Statistical Decision-Making Relevant in
More informationAn Adaptive Detector with Range Estimation Capabilities for Partially Homogeneous Environment
IEEE SIGNAL PROCESSING LETTERS VOL. 21 NO. 3 MARCH 2014 325 An Adaptive Detector Range Estimation Capabilities for Partially Homogeneous Environment A. De Maio Fellow IEEE C.Hao Member IEEE D. Orlo Senior
More informationPlug-in Measure-Transformed Quasi Likelihood Ratio Test for Random Signal Detection
Plug-in Measure-Transformed Quasi Likelihood Ratio Test for Random Signal Detection Nir Halay and Koby Todros Dept. of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel February 13, 2017 1 /
More informationTime Reversal Transmission in MIMO Radar
Time Reversal Transmission in MIMO Radar Yuanwei Jin, José M.F. Moura, and Nicholas O Donoughue Electrical and Computer Engineering Carnegie Mellon University Pittsburgh, PA 53 Abstract Time reversal explores
More informationTARGET DETECTION WITH FUNCTION OF COVARIANCE MATRICES UNDER CLUTTER ENVIRONMENT
TARGET DETECTION WITH FUNCTION OF COVARIANCE MATRICES UNDER CLUTTER ENVIRONMENT Feng Lin, Robert C. Qiu, James P. Browning, Michael C. Wicks Cognitive Radio Institute, Department of Electrical and Computer
More information2. What are the tradeoffs among different measures of error (e.g. probability of false alarm, probability of miss, etc.)?
ECE 830 / CS 76 Spring 06 Instructors: R. Willett & R. Nowak Lecture 3: Likelihood ratio tests, Neyman-Pearson detectors, ROC curves, and sufficient statistics Executive summary In the last lecture we
More informationMIMO Radar Space-Time Adaptive Processing Using Prolate Spheroidal Wave Functions
1 MIMO Radar Space-Time Adaptive Processing Using Prolate Spheroidal Wave Functions Chun-Yang Chen and P. P. Vaidyanathan, Fellow, IEEE Abstract In the traditional transmitting beamforming radar system,
More informationHyung So0 Kim and Alfred 0. Hero
WHEN IS A MAXIMAL INVARIANT HYPOTHESIS TEST BETTER THAN THE GLRT? Hyung So0 Kim and Alfred 0. Hero Department of Electrical Engineering and Computer Science University of Michigan, Ann Arbor, MI 48109-2122
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 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 informationSpace-Time Adaptive Processing: Algorithms
Wolfram Bürger Research Institute for igh-frequency Physics and Radar Techniques (FR) Research Establishment for Applied Science (FGAN) Neuenahrer Str. 2, D-53343 Wachtberg GERMANY buerger@fgan.de ABSTRACT
More informationIEEE copyright notice
IEEE copyright notice Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for
More informationResponse Vector Constrained Robust LCMV. Beamforming Based on Semidefinite Programming
1.119/TSP.215.246221, IEEE Transactions on Signal Processing Response Vector Constrained Robust LCMV 1 Beamforming Based on Semidefinite Programming Jingwei Xu, Guisheng Liao, Member, IEEE, Shengqi Zhu,
More informationALARGE class of modern array processing techniques are
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 52, NO. 6, JUNE 2004 1537 Detection of Distributed Sources Using Sensor Arrays Yuanwei Jin, Member, IEEE, and Benjamin Friedlander, Fellow, IEEE Abstract In
More informationADAPTIVE RADAR DETECTION
TESI DI DOTTORATO UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II DIPARTIMENTO DI INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI DOTTORATO DI RICERCA IN INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI
More informationMODEL ORDER ESTIMATION FOR ADAPTIVE RADAR CLUTTER CANCELLATION. Kelly Hall, 4 East Alumni Ave. Kingston, RI 02881
MODEL ORDER ESTIMATION FOR ADAPTIVE RADAR CLUTTER CANCELLATION Muralidhar Rangaswamy 1, Steven Kay 2, Cuichun Xu 2, and Freeman C. Lin 3 1 Air Force Research Laboratory, Sensors Directorate, 80 Scott Drive,
More informationCFAR DETECTION OF SPATIALLY DISTRIBUTED TARGETS IN K- DISTRIBUTED CLUTTER WITH UNKNOWN PARAMETERS
CFAR DETECTION OF SPATIALLY DISTRIBUTED TARGETS IN K- DISTRIBUTED CLUTTER WITH UNKNOWN PARAMETERS N. Nouar and A.Farrouki SISCOM Laboratory, Department of Electrical Engineering, University of Constantine,
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 informationAIR FORCE INSTITUTE OF TECHNOLOGY
AFIT/GE/ENG/04-02 FORWARD LOOKING RADAR: INTERFERENCE MODELLING, CHARACTERIZATION, AND SUPPRESSION THESIS James T. Caldwell Second Lieutenant, USAF AFIT/GE/ENG/04-02 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY
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 informationAD- A javal Research Laboratory. convergence performance of Akdaptive Detectors, part 4. Radar D ivislofl E E19930 NRVJIFB53419*2,9M
javal Research Laboratory aeshirtgtoni DC 20375-5320 NRVJIFB5349*2,9M AD- A25 9 225 convergence performance of Akdaptive Detectors, part 4 y,4r GERLACII AD : C.LI D T I Target Characteristics BranchAE
More informationDetection of Anomalies in Texture Images using Multi-Resolution Features
Detection of Anomalies in Texture Images using Multi-Resolution Features Electrical Engineering Department Supervisor: Prof. Israel Cohen Outline Introduction 1 Introduction Anomaly Detection Texture Segmentation
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 informationRecipes for the Linear Analysis of EEG and applications
Recipes for the Linear Analysis of EEG and applications Paul Sajda Department of Biomedical Engineering Columbia University Can we read the brain non-invasively and in real-time? decoder 1001110 if YES
More informationMeasure-Transformed Quasi Maximum Likelihood Estimation
Measure-Transformed Quasi Maximum Likelihood Estimation 1 Koby Todros and Alfred O. Hero Abstract In this paper, we consider the problem of estimating a deterministic vector parameter when the likelihood
More informationIntroduction to Statistical Inference
Structural Health Monitoring Using Statistical Pattern Recognition Introduction to Statistical Inference Presented by Charles R. Farrar, Ph.D., P.E. Outline Introduce statistical decision making for Structural
More informationA NOVEL COMPRESSED SENSING BASED METHOD FOR SPACE TIME SIGNAL PROCESSING FOR AIR- BORNE RADARS
Progress In Electromagnetics Research B, Vol. 52, 139 163, 2013 A NOVEL COMPRESSED SENSING BASED METHOD FOR SPACE TIME SIGNAL PROCESSING FOR AIR- BORNE RADARS Jing Liu *, Chongzhao Han, Xianghua Yao, and
More informationIEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 2, FEBRUARY
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 2, FEBRUARY 2005 427 The Adaptive Coherence Estimator: A Uniformly Most-Powerful-Invariant Adaptive Detection Statistic Shawn Kraut, Member, IEEE, Louis
More informationArray Signal Processing Algorithms for Beamforming and Direction Finding
Array Signal Processing Algorithms for Beamforming and Direction Finding This thesis is submitted in partial fulfilment of the requirements for Doctor of Philosophy (Ph.D.) Lei Wang Communications Research
More informationImproved Unitary Root-MUSIC for DOA Estimation Based on Pseudo-Noise Resampling
140 IEEE SIGNAL PROCESSING LETTERS, VOL. 21, NO. 2, FEBRUARY 2014 Improved Unitary Root-MUSIC for DOA Estimation Based on Pseudo-Noise Resampling Cheng Qian, Lei Huang, and H. C. So Abstract A novel pseudo-noise
More informationUNIFORMLY MOST POWERFUL CYCLIC PERMUTATION INVARIANT DETECTION FOR DISCRETE-TIME SIGNALS
UNIFORMLY MOST POWERFUL CYCLIC PERMUTATION INVARIANT DETECTION FOR DISCRETE-TIME SIGNALS F. C. Nicolls and G. de Jager Department of Electrical Engineering, University of Cape Town Rondebosch 77, South
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 informationErgodic and Outage Capacity of Narrowband MIMO Gaussian Channels
Ergodic and Outage Capacity of Narrowband MIMO Gaussian Channels Yang Wen Liang Department of Electrical and Computer Engineering The University of British Columbia April 19th, 005 Outline of Presentation
More informationAcoustic Source Separation with Microphone Arrays CCNY
Acoustic Source Separation with Microphone Arrays Lucas C. Parra Biomedical Engineering Department City College of New York CCNY Craig Fancourt Clay Spence Chris Alvino Montreal Workshop, Nov 6, 2004 Blind
More informationWavelet Methods for Time Series Analysis. Part IV: Wavelet-Based Decorrelation of Time Series
Wavelet Methods for Time Series Analysis Part IV: Wavelet-Based Decorrelation of Time Series DWT well-suited for decorrelating certain time series, including ones generated from a fractionally differenced
More informationOptimum Passive Beamforming in Relation to Active-Passive Data Fusion
Optimum Passive Beamforming in Relation to Active-Passive Data Fusion Bryan A. Yocom Applied Research Laboratories The University of Texas at Austin Final Project EE381K-14 Multidimensional Digital Signal
More informationSENSOR ERROR MODEL FOR A UNIFORM LINEAR ARRAY. Aditya Gadre, Michael Roan, Daniel Stilwell. acas
SNSOR RROR MODL FOR A UNIFORM LINAR ARRAY Aditya Gadre, Michael Roan, Daniel Stilwell acas Virginia Center for Autonomous Systems Virginia Polytechnic Institute & State University Blacksburg, VA 24060
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 informationAnalysis of Random Radar Networks
Analysis of Random Radar Networks Rani Daher, Ravira Adve Department of Electrical and Computer Engineering, University of Toronto 1 King s College Road, Toronto, ON M5S3G4 Email: rani.daher@utoronto.ca,
More informationSpace-Time Adaptive Signal Processing for Sea Surveillance Radars
reg. number : 2008telb0075 Thesis presented at the Military University of Technology in Warsaw with the authorisation of the University of Rennes 1 to obtain the degree of Doctor of Philosophy in association
More informationBistatic Space-Time Adaptive Processing for Ground Moving Target Indication
Bistatic Space-Time Adaptive Processing for Ground Moving Target Indication Chin-Heng Lim E H U N I V E R S I T Y T O H F R G E D I N B U A thesis submitted for the degree of Doctor of Philosophy. The
More information