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FAM和SSCA算法的matlab源程序-detection and identification of signalFAM和SSCA算法的matlab源程序-detection and identification of signalDISCLAIMER NOTICEMTHIS DOCUMENT IS BESTQUALITY AVAILABLE. THECOPY FURNISHED TO DTICCONTAINED A SIGNIFICANTNUMBER OF PAGES WHICH DONOT REPRODUCE LEGIBLY.ilApproved for public release; distribution is unlimitedDETECTION AND IDENTIFICATION OF CYCLOSTATIONARY SIGNALSEvandro luiz da costaLieutenant Commander, brazilian NavyB.S., Instituto Militar de Engenharia, 1980Submitted in partial fulfillmentof the requirements for the degree ofMASTER OF SCIENCE IN ELECTRICAL ENGINEERINGANDMASTER OF SCIENCE IN ENGINEERING ACOUSTICSfrom theNAVAL POSTGRADUATE SCHOOLMarch 1996Author.Evandrodk da costaapproved byRQ求Ralph Hippenstiel, Thesis Co-AdvisorRoberto Cristi, Thesis Ca-AdvisoiHerschel. Loomis, Jr, hairmanDepartment of Electrical and Computer EngineeringArthony A. Atchley / ChairmanEngineering Acoustics Academic CommitteeABSTRACTPropeller noise can be modeled as an amplitude modulated(AM) signalCyclic Spectral Analysis has been used successfully to detect the presence ofanalog and digitally modulated signals in communication systems. It can also identithe type of modulation. Programs for Signal Processing based on compiledlanguages such as FORTRAN or C are not user friendly, and MATLAB basedprograms have become the de facto language and tools for signal processingengineers worldwideThis thesis describes the implementation in mAtlab of two fast methods ofcomputing the Spectral Correlation Density(SCD)Function estimate, the FFTAccumulation Method (FAM)and the Strip Spectral Correlation Algorithm( SSCA),toperform Cyclic Analysis. Both methods are based on the Fast Fourier transformFFT)algorithm. The results are presented and areas of possible enhancement forpropeller noise detection and identification are discussedTABLE OF CONTENTSINTRODUCTIONA MOTIVATION,P,中“····*s···:···:B BACKGROUNDC THESIS GOALSIL NOISE IN THE OCEAN··+A TYPES OF UNDERWATER NOISE25561. Ambient Noise番申2. Self noise3. Radiated noise8B RADIATED NOISE FROM SHIPS, SUBMARINES AND TORPEDOES.......8C PROPELLER NOISE10lI CYCLOSTATIONARY PROCESSING15A CYCLOSTATIONARIT15B THE CYCLIC AUTOCORRELATION FUNCTION (ACF)17C THE SPECTRAL CORRELATION DENSITY FUNCTION (SCD)18N. ESTIMATION OF THE SPECTRAL CORRELATION DENSITY FUNCTION23A FFT ACCUMULATION METHOD(FAM)25B STRIP SPECTRAL CORRELATION ALGORITHM(SSCA■D28V. EXPERIMENTAL RESULTSA. ANALOG-MODULATED SIGNALS311. Amplitude Modulated(AM)Signal===2-312. Pulse-Amplitude Modulated(PAM) Signal58B DIGITAL-MODULATED SIGNALS1. Amplitude Shift Keying(ASK) Signal中中非昏号即即号自唱即自曲音非带卡.最662. Binary-Phase Shift Keying(BPSK) Signal6了VI CONCLUSIONS81A SUMMARY81B SUGGESTIONS82APPENDIX A-CALCULATION OF THE SCD FUNCTION OF AN AMPLITUDE-MODULATED SIGNAL83APPENDIX B-FUNCTION AUTOFAM95APPENDIX C-FUNCTION AUTOSSCA99APPENDIXD-FUNCTION CROSSFAM103APPENDIX E-FUNCTION CROSSSSCA109APPENDIX F-PLOTTING ROUTINES113LIST OF REFERENCES115INITIAL DISTRIBUTION LIST看音117INTRODUCTIONA. MOTIVATIONPropeller related acoustic signatures typically exhibit modulationcharacteristics. These modulation characteristics originate from the cavitationprocess that takes place in the water due to the cyclic movement of the propellerThe cavitation process is basically the collapse of air and vapor bubblesdue to variations in the static pressure. These variations in static pressure are aconsequence of the passage of the propeller blades through the water. Thismovement, cyclic in nature, causes amplitude modulation in the static pressureand as a consequence an amplitude-modulated(AM) signal can be detected in areceiverCyclostationary processing techniques have been used to detect andlentify analog and digital communication signals very successfully. Thesetechniques have the advantage of using a more realistic model for the signalthan the stationary model used in most of the more conventional signalprocessing techniquesB BACKGROUNDThe basic elements of cyclic spectral analysis are the time-variant cyclicperiodogram and the time-variant cyclic correlogram. These two functions form aFourier transform pair. This fact is known as the cyclic Wiener relation or thecyclic Wiener-Khinchin relation [Ref. 1: p. 49.1
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【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138
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