digital signal processing by ramesh babu pdf free download

... 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 ... serves c  1999 by CRC Press LLC Space-TimeAdaptiveProcessing forAirborneSurveillanceRadar HongWang SyracuseUniversity 70.1MainReceiveApertureandAnalogBeamforming 70.2DatatobeProcessed 70.3TheProcessingNeedsandMajorIssues 70.4TemporalDOFReduction 70.5AdaptiveFilteringwithNeededandSample-Supportable DOFandEmbeddedCFARProcessing 70.6Scan-To-ScanTrack-Before-DetectProcessing 70.7Real-TimeNonhomogeneityDetectionandSample ConditioningandSelection 70.8SpaceorSpace-RangeAdaptivePre-SuppressionofJammers 70.9ASTAPExamplewithaRevisittoAnalogBeamforming 70.10Summary References Space-TimeAdaptiveProcessing(STAP)isamulti-dimensionalfilteringtechniquedevelopedfor minimizingtheeffectsofvariouskindsofinterferenceontargetdetectionwithapulsedairborne surveillanceradar.Themostcommondimensions,orfilteringdomains,generallyincludetheaz- imuthangle,elevationangle,polarizationangle,dopplerfrequency,etc.inwhichtherelativelyweak targetsignaltobedetectedandtheinterferencehavecertaindifferences.Inthefollowing,theSTAP principlewillbeillustratedforfilteringinthejointazimuthangle(space)anddopplerfrequency (time)domainonly. STAPhasbeenaveryactiveresearchanddevelopmentareasincethepublicationofReedetal.’s seminalpaper[1].WiththerecentlycompletedMultichannelAirborneRadarMeasurementproject (MCARM)[2]–[5],STAPhasbeenestablishedasavaluablealternativetothetraditionalapproaches, suchasultra-lowsidelobebeamformingandDisplacedPhaseCenterAntenna(DPCA)[6].Muchof STAPresearchanddevelopmenteffortshavebeendrivenbytheneedstomakethesystemaffordable, tosimplifyitsfront-hardwarecalibration,andtominimizethesystem’sperformancelossinseverely nonhomogeneousenvironments.Figure70.1isageneralconfigurationofSTAPfunctionalblocks[5, 7]whoseprincipleswillbediscussedinthefollowingsections. c  1999byCRCPressLLC ...

... IntroductionandMotivation Thischapterreviewstheapplicationsofantennaarraysignalprocessingtomobilenetworks.Cellular networksarerapidlygrowingaroundtheworldandanumberofemergingtechnologiesareseentobe criticaltotheirimprovedeconomicsandperformance.Amongtheseistheuseofmultipleantennas andspatialsignalprocessingatthebasestation.ThistechnologyisreferredtoasSmartAntennasor, moreaccurately,asSpace-TimeProcessing(STP).STPreferstoprocessingtheantennaoutputsin bothspaceandtimetomaximizesignalquality. Acellulararchitectureisusedinanumberofmobile/portablecommunicationsapplications.Cell sizesmayrangefromlargemacrocells,whichservehighspeedmobiles,tosmallermicrocellsorvery smallpicocells,whicharedesignedforoutdoorandindoorapplications.Eachoftheseoffersdifferent channelcharacteristicsand,therefore,posesdifferentchallengesforSTP.Likewise,differentservice deliverygoalssuchasgradeofserviceandtypeofservice:voice,data,orvideo,alsoneedspecic STPsolutions.STPprovidesthreeprocessingleverages.Therstisarraygain.Multipleantennas capturemoresignalenergy,whichcanbecombinedtoimprovethesignal-to-noiseratio(SNR).Next isspatialdiversitytocombatspace-selectivefading.Finally,STPcanreduceco-channel,adjacent channel,andinter-symbolinterference. Theorganizationofthischapterisasfollows.InSection68.2,wedescribethevectorchannelmodel forabasestationantennaarray.InSection68.3wediscussthealgorithmsforSTP.Section68.4outlines theapplicationsofSTPincellularnetworks.Finally,weconcludewithasummaryinSection68.5. c 1999byCRCPressLLC ... the blind identification of multichannel FIR filters, IEEE Trans. on Signal Processing, 1995. [29] Orfanidis, S.J., Optimal Signal Processing – An Introduction, Macmillan Publishing Co., New York, ... constant modulus signals: a bilinear approach, in Proc. VII European Signal Processing Conference, Edinburgh, Scotland, Sept. 13-16, 1994. [33] Porat, B. and Friedlander, B., Blind equalization of digital...

... computation further by 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) ... a Signal Eigenstructure (SE) update because only the dominant r signal eigencomponents are tracked. This case is equivalent to that described by Schreiber [37] and an SVD version is given by ... by CRC Press LLC R. D. De Groat, et. Al. “Subspace Tracking.” 2000 CRC Press LLC. <http://www.engnetbase.com>. is bounded by a constant, assuming no significant errors are introduced by...

... . (23.14) c  1999 by CRC Press LLC Williamson, G.A. “Adaptive IIR Filters” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC ... systems, IEEE Trans. Signal Processing, 41(2), 617–628, 1993. [14] Lin, J N. and Unbehauen, R., Bias-remedy least mean square equation error algorithm for IIR parameter 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,...

... e a (i) by à(i) before ltering it by the lter F and then scaling the result by à(i)/ à(i). Similarly, the term à(i) v(i) is rst divided by à(i), then ltered by F, and is nally scaled by à(i)/ à(i). FIGURE ... Ling, F., and Nikias, C.L., Advanced Digital Signal Processing , Macmillan Publishing, New York, 1992. [3] Widrow, B. and Stearns, S.D., Adaptive Signal Processing , Prentice-Hall, Englewood ... of the input sequence u i c  1999 by CRC Press LLC This relation establishes a lossless map (denoted by T i ) from the signals {w i1 , à(i) r(i)} to the signals {w i , à(i) e a (i)}. Correspondingly,...

... (19.2) whereW(n)=[w 0 (n)w 1 (n)ÃÃÃw L1 (n)] T isthecoefcientvector,X(n)=[x(n)x(n 1)ÃÃÃx(nL+1)] T istheinputsignalvector,d(n)isthedesiredsignal,e(n)istheerrorsignal, andà(n)isthestepsize. TherearethreemainreasonswhytheLMSadaptivelterissopopular.First,itisrelativelyeasyto implementinsoftwareandhardwareduetoitscomputationalsimplicityandefcientuseofmemory. Second,itperformsrobustlyinthepresenceofnumericalerrorscausedbynite-precisionarithmetic. Third,itsbehaviorhasbeenanalyticallycharacterizedtothepointwhereausercaneasilysetupthe systemtoobtainadequateperformancewithonlylimitedknowledgeabouttheinputanddesired responsesignals. c 1999byCRCPressLLC [22] Farden,D.C., Tracking properties of adaptivesignal processingalgorithms, IEEETrans.Acoust., Speech, Signal Processing, ASSP-29(3), ... algorithms, IEEE Trans. Signal Processing, 41(9), 2811–2825, Sept. 1993. [25] Douglas, S.C. and Meng, T.H Y., Normalized data nonlinearities for LMS adaptation, IEEE Trans. Signal Processing, 42(6), ... Contr. Signal Processing, 4(3), 219–216, May-June 1990. [28] Harris, R.W., Chabries, D.M., and Bishop, F.A., A variable step (VS) adaptive filter algorithm, IEEE Trans. Acoust., Speech, Signal Processing, ASSP-34(2),...

... steps: c  1999 by CRC Press LLC [7] Djuri ´ c, P.M. and Li, H T., Bayesian spectrum estimation of harmonic signals, Signal Process. Lett., Vol. 2, pp. 213–215, 1995. [8] Hayes, M.S., Statistical Digital Signal ... = 1 λ i      N−1  n=0 x[n]w i [n]e −j2πf n      2 (14.70) c  1999 by CRC Press LLC Djuric, P.M. & Kay S.M. “Spectrum Estimation and Modeling” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas ... if ∞  n=−∞ |g[n]| < ∞ . (14.8) The signal {g[n]} can be determined from G(f ) by the inverse DTFT defined by g[n]=  1 0 G(f )e j2πf n df (14.9) which means that the signal {g[n]} can be represented...

... Temporal Signals in Gaussian Noise Signal Detection: Known Gains ã Signal Detection: Unknown Gains ã Signal Detection: Random Gains ã Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: ... 13.4. c  1999 by CRC Press LLC Hero, A. Signal Detection and Classification” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC ... waveform. Signal detection algo- rithms decide whether the waveform consists of “noise alone” or signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs...

... full refund. The Scientist and Engineer's Guide to Digital Signal Processing Second Edition by Steven W. Smith copyright â 1997-1999 by California Technical Publishing All rights reserved. ... 1 Telecommunications 4 Audio Processing 5 Echo Location 7 Imaging Processing 9 Chapter 2. Statistics, Probability and Noise 11 Signal and Graph Terminology 11 Mean and Standard Deviation 13 Signal vs. Underlying ... 80 Execution Speed: Programming Tips 84 The Scientist and Engineer's Guide to Digital Signal Processing Second Edition by Steven W. Smith California Technical Publishing San Diego, California xi Another...