... Signalsin the Time Domain11.1 Introduction Digital signalprocessing is concerned with the processing of a discrete-time signal, calledthe input signal, to develop another discrete-time signal, ... .Project 1.5 Signal SmoothingA common example of a digitalsignalprocessing application is the removal of the noisecomponent from a signal corrupted by additive noise. Let s[n] be the signal corrupted ... indicated earlier, the purpose of digitalsignalprocessing is to generate a signal withmore desirable properties from one or more given discrete-time signals. The processing algorithm consists...
... Mathematical Summary for Digital Signal Processing Applications with Matlab Dedicated to my son G.V. Vasig and my wifeG. Viji Contents1 Matrices ... 1.12.A D24123 4557 126 7 10 1635 E.S. GopiMathematical Summaryfor DigitalSignal Processing Applications with Matlab 123 1.15 Solutions for the System of Linear Equation [A] x Db 352445635T0@b2414253635Äcx1bx21AD ... obtained using Gram-Schmidtorthogonalization procedure.Example 1.18. Consider the matrix A D266415102610371148123775whose column vectors areindependent to each other. Using Gram-Schmidt,...
... The Scientist and Engineer's Guide to Digital Signal Processing Second Edition xiiPrefaceGoals and Strategies of this BookThe technical world is changing ... 209Chapter 12. The Fast Fourier Transform 225 Chapter 13. Continuous SignalProcessing 243 DIGITAL FILTERSChapter 14. Introduction to Digital Filters 261Chapter 15. Moving Average Filters 277Chapter ... 1Telecommunications 4Audio Processing 5Echo Location 7Imaging Processing 9Chapter 2. Statistics, Probability and Noise 11 Signal and Graph Terminology 11Mean and Standard Deviation 13 Signal vs. Underlying...
... Rao, C.R., Effects of estimated noise covariance matrix in optimal signal detection,IEEE Trans. on Acoustics, Speech, and Signal Processing, ASSP-35(5), 671–679,May 1987.[21] Cai, L.andWang, ... algorithm, Proc. Signal and Data Processing of Small Targets, SPIE Proc. Series, Vol. 1305, Paper 16, 180–192, Orlando,FL, April 16-18, 1990.[27] Wang, H. and Cai, L., On adaptive multiband signal detection ... May 1991.[16] DiPietro, R.C., Extended factored space-time processing for airborne radar systems,Proc. 26thAsilomar Conference on Signals, Systems, and Computers,425–430, Pacific Grove, CA,...
... multiusercommunications,IEEE Trans. on Signal Processing, Special Issue on SignalProcessing forAdvanced Communications,45(1), Jan. 1997.[10] Ding, Z., Blind channel identification and equalization using spectral ... IntroductionandMotivationThischapterreviewstheapplicationsofantennaarraysignalprocessingtomobilenetworks.Cellularnetworksarerapidlygrowingaroundtheworldandanumberofemergingtechnologiesareseentobecriticaltotheirimprovedeconomicsandperformance.Amongtheseistheuseofmultipleantennasandspatialsignalprocessingatthebasestation.ThistechnologyisreferredtoasSmartAntennasor,moreaccurately,asSpace-TimeProcessing(STP).STPreferstoprocessingtheantennaoutputsinbothspaceandtimetomaximizesignalquality.Acellulararchitectureisusedinanumberofmobile/portablecommunicationsapplications.Cellsizesmayrangefromlargemacrocells,whichservehighspeedmobiles,tosmallermicrocellsorverysmallpicocells,whicharedesignedforoutdoorandindoorapplications.Eachoftheseoffersdifferentchannelcharacteristicsand,therefore,posesdifferentchallengesforSTP.Likewise,differentservicedeliverygoalssuchasgradeofserviceandtypeofservice:voice,data,orvideo,alsoneedspecicSTPsolutions.STPprovidesthreeprocessingleverages.Therstisarraygain.Multipleantennascapturemoresignalenergy,whichcanbecombinedtoimprovethesignal-to-noiseratio(SNR).Nextisspatialdiversitytocombatspace-selectivefading.Finally,STPcanreduceco-channel,adjacentchannel,andinter-symbolinterference.Theorganizationofthischapterisasfollows.InSection68.2,wedescribethevectorchannelmodelforabasestationantennaarray.InSection68.3wediscussthealgorithmsforSTP.Section68.4outlinestheapplicationsofSTPincellularnetworks.Finally,weconcludewithasummaryinSection68.5.c1999byCRCPressLLC ... Paulraj, A., A constant modulus algorithm for multi-user signal separationin presenceof delayspread using antenna arrays,IEEE SignalProcessing Letters,4(6): 178–181,June 1997.[32] Papadias,...
... Some algorithms for eigensubspaceestimation, Digital Signal Processing, 5, 97–115, 1995.[36] Regalia, P.A. and Loubaton, P., Rational subspaceestimation using adaptivelossless filters,IEEETrans. ... extending this concept to the signal subspace [8]. By sphericalizing and deflating both the signal and the noise subspaces, the cost of tracking the r dimensional signal (or noise) subspace is ... the signal subspace is not sphericalized andall of its eigencomponents are explicitly tracked whereas the noise subspace is sphericalized and notexplicitly tracked (to save computation). Using...
... of three compo-nents: the signal ym(n) that is modeled by the adaptive filter, the signal yu(n) that is unmodeled butthat depends on the input signal, and the signal v(n) that is independent ... systems,IEEETrans. Signal Processing, 41(2), 617–628, 1993.[14] Lin, J N. and Unbehauen, R., Bias-remedy least mean square equation error algorithm for IIRparameter recursive estimation,IEEE Trans. Signal Processing, 40(1), ... Trans. Acoustics, Speech, Signal Processing, 38(7), 1222–1227,1990.[17] Regalia, P.A., Stable and efficient latticealgorithms for adaptive IIR filtering,IEEE Trans .Signal Processing, 40(2), 375–388,...
... the signals {wi1,à(i) r(i)} tothe signals {wi,à(i) ea(i)}. Correspondingly, using (20.12), the map from the original weighteddisturbanceà(i)v(i) to the weighted estimation error signal à(i)ea(i) ... S.,Adaptive Filter Theory, 3rd ed., Prentice-Hall, Englewood Cliffs, NJ, 1996.[2] Proakis, J.G., Rader, C.M., Ling, F., and Nikias, C.L.,Advanced DigitalSignal Processing ,Macmillan Publishing, ... Stearns, S.D.,Adaptive Signal Processing , Prentice-Hall, Englewood Cliffs, NJ,1985.[4] Sayed, A.H. and Kailath, T., A state-space approach to adaptive RLS filtering,IEEE Signal Processing Magazine,...
... T., Bayesian spectrum estimation of harmonic signals, Signal Process.Lett.,Vol. 2, pp. 213–215, 1995.[8] Hayes, M.S.,Statistical DigitalSignalProcessing and Modeling,John Wiley & Sons, ... performance of some other signal and noise subspace based methods developed later.14.5.5 Multiple Signal Classification (MUSIC)A procedure very similar to Pisarenko’s is the MUltiple SIgnal Classification ... which plays a majorrole in many applied sciences such as radar, speech processing, underwater acoustics, biomedical signal processing, sonar, seismology, vibration analysis, control theory, and...
... Temporal Signals in Gaussian Noise Signal Detection: Known Gainsã Signal Detection: UnknownGainsã Signal Detection: Random Gainsã Signal Detection:Single Signal 13.6 Spatio-Temporal SignalsDetection: ... SpatialCovariance13.7 Signal ClassicationClassifying Individual SignalsãClassifying Presence of Multi-ple SignalsReferences13.1 IntroductionDetection and classification arise in signalprocessing ... incorporate nonzeromean [14, 15].13.7 Signal ClassificationTypical classification problems arising in signalprocessing are: classifying an individual signal wave-form out of a set of possible...