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reg. number : 2008telb0075Thesispresented at theMilitary University of Technology in Warsawwith the authorisation of the University of Rennes 1to obtain the degree ofDoctor of Philosophy in association with TelecomBretagne and the Military University of TechnologyDomain : Signal Processing and TelecommunicationsMention : Traitement du Signal et TlcommunicationbyTomasz GrskiUniversities : Telecom Bretagne and the Military University of Technology in WarsawSpace-Time Adaptive Signal Processing for SeaSurveillance RadarsDefence December 9, 2008 before the examination board :Reporters : Marc Acheroy, Professor at Royal Military Academy in BrusselsRichard Klemm, Doctor at FGANExaminers : Jean Marc Le-Caillec, Professor at Telecom BretagneAdam Kawalec, Professor at the Military University of Technology in WarsawLaurent Ferro-Famil, Doctor with accreditation to supervise reasearchat the University of Rennes 1Ali Khenchaf, Professor at ENSIETAContents1 Introduction. 12 Radar Basics, Space-Time Adaptive Processing and Target Detection. 32.1 Radar principles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Overview of STAP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.1 Problem Statement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.2 Radar System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.3 Airborne Clutter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.4 Adaptive MTI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.5 STAP Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.6 Assumptions and Limitations. . . . . . . . . . . . . . . . . . . . . . . . 182.3 Detection Principles: Neyman-Pearson Test. . . . . . . . . . . . . . . . . . . . 192.3.1 Notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.2 Neyman-Pearson Lemma. . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.3 Generalized Likelihood Ratio Test. . . . . . . . . . . . . . . . . . . . . 202.3.4 Alternative Hypothesis of the Form > H0. . . . . . . . . . . . . . . 212.3.5 Alternative Hypothesis of the Form ,= H0. . . . . . . . . . . . . . . 212.4 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.4.1 Detection of Known Narrowband Signals in Narrowband Noise. . . . . 222.4.2 Detection of Known Narrowband Signals with Random Phase Angles. 232.5 Spherically Invariant Random Process (SIRP). . . . . . . . . . . . . . . . . . 242.6 Likelihood Ratio Test and Generalized Likelihood Ratio Test applied to theSpherically Invariant Random Process. . . . . . . . . . . . . . . . . . . . . . . 252.6.1 Detection of Known Narrowband Signals - Likelihood Ratio Test. . . . 252.6.2 Detection of Known Narrowband Signals with Random Phase Anglesand Random Amplitude - GLRT Detector. . . . . . . . . . . . . . . . 272.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Sea Clutter. 31CONTENTS ii3.1 Sea clutter characterization in X band. . . . . . . . . . . . . . . . . . . . . . . 323.2 Sea clutter characterization in HF band. . . . . . . . . . . . . . . . . . . . . . 363.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 Two Dirac delta detector. 504.1 Resolving GLRT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.2 Two Dirac Deltas approximation. . . . . . . . . . . . . . . . . . . . . . . . . . 514.2.1 First approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2.2 Rened approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.3 Simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564.3.1 Simulation parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . 564.3.2 Target Simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.3.3 Additive Noise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.4.1 Classical STAP detection performance evaluation. . . . . . . . . . . . 654.4.2 Numerical simplications for TDD STAP. . . . . . . . . . . . . . . . . 664.4.3 Comparison of classical STAP and TDD STAP for xed parameter. 704.4.4 Results for TDD STAP detector with automatic nding. . . . . . . 734.5 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 HF radar signals experiments and STAP technique modications. 765.1 WERA radar system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765.2 Implementation of Adaptive MTI and STAP - covariance matrix estimationproblem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805.2.1 Adaptive MTI implementation . . . . . . . . . . . . . . . . . . . . . . 805.2.2 STAP implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 875.3 Comparisons between the results of AMTI and STAP . . . . . . . . . . . . . 885.3.1 Data le and target description. . . . . . . . . . . . . . . . . . . . . . 885.3.2 Detection of the tug ship from Garchine radar site. . . . . . . . . . . . 895.3.3 Detection of the tug ship from Brezzelec radar site. . . . . . . . . . . . 915.3.4 Detection of the shery ship from Garchine radar site. . . . . . . . . . 925.3.5 Detection of the shery ship from Brezzelec radar site. . . . . . . . . . 975.4 Thresholding and detections presentation. . . . . . . . . . . . . . . . . . . . . 1005.5 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1016 Conclusions and perspectives 104A Gaussian complex process. 106CONTENTS iiiB Data generation. 108C Space Time Adaptive Processing based on Frequency Modulated Contin-uous Wave system. 110C.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110C.2 Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110C.3 Antenna array with FMCW. . . . . . . . . . . . . . . . . . . . . . . . . . . . 119C.4 STAP system using FMCW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121C.5 FMCW HF system - practical example. . . . . . . . . . . . . . . . . . . . . . 122C.6 FMCW X-band system - practical example. . . . . . . . . . . . . . . . . . . . 123C.7 FMCW L-band system - practical example. . . . . . . . . . . . . . . . . . . . 124D List of symbols and abbreviations. 125Bibliography 127CHAPTER1Introduction.Present radar systems for sea surveillance have several limitations. One group of limitationsis related to strong clutter from sea waves (especially during heavy seas periods). Anothergroup is related to range limitations of present microwave systems. These are big obstacleshindering to provide reliable surveillance data that cover Exclu