FAM和SSCA算法的matlab源程序-detection and identification of signal

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FAM和SSCA算法的matlab源程序-detection and identification of signal FAM和SSCA算法的matlab源程序-detection and identification of signal
DISCLAIMER NOTICE M THIS DOCUMENT IS BEST QUALITY AVAILABLE. THE COPY FURNISHED TO DTIC CONTAINED A SIGNIFICANT NUMBER OF PAGES WHICH DO NOT REPRODUCE LEGIBLY. il Approved for public release; distribution is unlimited DETECTION AND IDENTIFICATION OF CYCLOSTATIONARY SIGNALS Evandro luiz da costa Lieutenant Commander, brazilian Navy B.S., Instituto Militar de Engenharia, 1980 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN ELECTRICAL ENGINEERING AND MASTER OF SCIENCE IN ENGINEERING ACOUSTICS from the NAVAL POSTGRADUATE SCHOOL March 1996 Author. Evandrodk da costa approved by RQ求 Ralph Hippenstiel, Thesis Co-Advisor Roberto Cristi, Thesis Ca-Advisoi Herschel. Loomis, Jr, hairman Department of Electrical and Computer Engineering Arthony A. Atchley / Chairman Engineering Acoustics Academic Committee ABSTRACT Propeller noise can be modeled as an amplitude modulated(AM) signal Cyclic Spectral Analysis has been used successfully to detect the presence of analog and digitally modulated signals in communication systems. It can also identi the type of modulation. Programs for Signal Processing based on compiled languages such as FORTRAN or C are not user friendly, and MATLAB based programs have become the de facto language and tools for signal processing engineers worldwide This thesis describes the implementation in mAtlab of two fast methods of computing the Spectral Correlation Density(SCD)Function estimate, the FFT Accumulation Method (FAM)and the Strip Spectral Correlation Algorithm( SSCA),to perform Cyclic Analysis. Both methods are based on the Fast Fourier transform FFT)algorithm. The results are presented and areas of possible enhancement for propeller noise detection and identification are discussed TABLE OF CONTENTS INTRODUCTION A MOTIVATION ,P, 中“····*s···:···: B BACKGROUND C THESIS GOALS IL NOISE IN THE OCEAN ··+ A TYPES OF UNDERWATER NOISE 2556 1. Ambient Noise 番申 2. Self noise 3. Radiated noise 8 B RADIATED NOISE FROM SHIPS, SUBMARINES AND TORPEDOES.......8 C PROPELLER NOISE 10 lI CYCLOSTATIONARY PROCESSING 15 A CYCLOSTATIONARIT 15 B THE CYCLIC AUTOCORRELATION FUNCTION (ACF) 17 C THE SPECTRAL CORRELATION DENSITY FUNCTION (SCD) 18 N. ESTIMATION OF THE SPECTRAL CORRELATION DENSITY FUNCTION 23 A FFT ACCUMULATION METHOD(FAM) 25 B STRIP SPECTRAL CORRELATION ALGORITHM(SSCA ■D 28 V. EXPERIMENTAL RESULTS A. ANALOG-MODULATED SIGNALS 31 1. Amplitude Modulated(AM)Signal ===2- 31 2. Pulse-Amplitude Modulated(PAM) Signal 58 B DIGITAL-MODULATED SIGNALS 1. Amplitude Shift Keying(ASK) Signal 中中非昏号即即号自唱即自曲音非带卡.最 66 2. Binary-Phase Shift Keying(BPSK) Signal 6了 VI CONCLUSIONS 81 A SUMMARY 81 B SUGGESTIONS 82 APPENDIX A-CALCULATION OF THE SCD FUNCTION OF AN AMPLITUDE-MODULATED SIGNAL 83 APPENDIX B-FUNCTION AUTOFAM 95 APPENDIX C-FUNCTION AUTOSSCA 99 APPENDIXD-FUNCTION CROSSFAM 103 APPENDIX E-FUNCTION CROSSSSCA 109 APPENDIX F-PLOTTING ROUTINES 113 LIST OF REFERENCES 115 INITIAL DISTRIBUTION LIST 看音 117 INTRODUCTION A. MOTIVATION Propeller related acoustic signatures typically exhibit modulation characteristics. These modulation characteristics originate from the cavitation process that takes place in the water due to the cyclic movement of the propeller The cavitation process is basically the collapse of air and vapor bubbles due to variations in the static pressure. These variations in static pressure are a consequence of the passage of the propeller blades through the water. This movement, cyclic in nature, causes amplitude modulation in the static pressure and as a consequence an amplitude-modulated(AM) signal can be detected in a receiver Cyclostationary processing techniques have been used to detect and lentify analog and digital communication signals very successfully. These techniques have the advantage of using a more realistic model for the signal than the stationary model used in most of the more conventional signal processing techniques B BACKGROUND The basic elements of cyclic spectral analysis are the time-variant cyclic periodogram and the time-variant cyclic correlogram. These two functions form a Fourier transform pair. This fact is known as the cyclic Wiener relation or the cyclic Wiener-Khinchin relation [Ref. 1: p. 49.1 【实例截图】
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