Warsaw MTI

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<p>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 &gt; 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 Exclusive Economic Zone (200nautical miles)124 hours a day, 365 days a year. Therefore a big challenge is to nd newtechniques for this application. This work covers signal processing for this purpose. Space-Time Adaptive Processing (STAP) technique has a relatively long history. The theory ofSTAP was rst published by Lawrence E. Brennan and Irving S. Reed in 1970s [6], whereascomplete monographies on this subject were published by Richard Klemm in 2002 and 2004[37, 38]. It is also worth to mention here contribution of the paper written by E. J. Kelly [36].Nevertheless applications are mainly related to target detection in the presence of clutterthat has Gaussian statistical properties. In this work it is proposed to apply this techniqueto target detection under non-Gaussian conditions.The thesis of this work can be formulated as follows: STAP can be an eective tech-nique for the Sea Surveillance Radars. Surveillance can be understood as clutter (andinterferences) suppression, target matching and threshold decision. To this end there are threeproblems that can be identied with regards to the sea clutter problem. These problems are:1. Clutter non-stationarity in space and time.2. Clutter non-Gaussianity.3. Clutter with spread Doppler spectrum.The purpose of this work is to evaluate dierent algorithms, to nd possible problems withimplementing them and to try to nd solutions to these problems. As a result this work servesas a complete guide how to deal with sea clutter by modifying STAP technique.In the rst chapter, reader can nd elementary radar concepts as well as an introductionto Space-Time Adaptive Processing. First section is devoted to basic radar concepts. Nextsection is an introduction to Adaptive MTI (AMTI) and STAP. It will be shown how STAPwas introduced for airborne radar, and what was rationale standing behind this. Generallywe can say, that the origin of STAP was the observation, that clutter spectrum depends onthe look angle of radar system. Assumptions and limitations of STAP will be shown in thesame chapter. In the next sections reader can nd some elements of detection theory andNeyman-Pearson Lemma and Test. This will build a base to derive more general detectorsthan STAP in chapter 4. In the same chapter theory of Spherically Invariant Random Process1EEZ zone was dened in 1982 by United Nations Convention in Montego Bay.CHAPTER 1. INTRODUCTION. 2(SIRP) is introduced. This theory is very useful when dealing with non-Gaussian clutter. Itwill be shown in the next chapter, that sea clutter very often has non-Gaussian propertiesand in this case we can employ theory of SIRP. Last section will be devoted to derivation ofNeymann-Pearson tests in the case of SIRP. It will be shown, that classical STAP algorithmcan be derived from this more general form.The second chapter is devoted entirely to sea clutter characterization. Two radar bandswill be considered: X-band and HF-band. In the rst section, X-band clutter Doppler-spectrum and its properties will be presented. It will be shown, that clutter Doppler shiftand statistics are related to geometry of the scene as well as many ocean parameters [9]. Thisproperty is exploited in STAP algorithm, therefore it is worth considering STAP techniqueto deal with this kind of clutter. Unfortunately X-band sea clutter (especially for low graz-ing angles) has non-Gaussian properties [8], whereas STAP was derived under assumptionof Gaussianity. Therefore it is possible to improve classical STAP algorithm to deal withsea clutter. This problem will be treated in chapter 4. For HF-band, sea clutter propertiesare dierent. The main contribution to the clutter is Bragg scattering. Its Doppler spec-trum remains the same across dierent look angles. Moreover, for HF-band it is very likelyto have strong radio interferences. Authors own calculations illustrating Bragg clutter andinterferences will be presented. Results were obtained using real data from WERA radar sys-tem. Again we can see, that clutter and interferences have two-dimensional, space and timestructure, and therefore it is reasonably to use STAP algorithm.Chapter 4 addresses the problem of the derivation of detectors under non-Gaussianitythat was raised in chapter 3. This is done in the framework of Spherically Invariant Ran-dom Process. A new detector will be presented. It can deal with non-Gaussian clutter andnoise. To evaluate performances of classical and new STAP detector in non-Gaussian clutter,I performed some simulations. Receiver Operation Curves (ROC) are presented based onsimulations made by the author. A discussion of the performances of usual STAP and theproposed detector under dierent kind of clutter (Gaussian...</p>