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2.2.LinearTime-InvariantSystems(WHYAbasicfact:IfweknowtheresponseofanLTItosomeinputs,weactuallyknowtheresponsetomanyKeypointsofSignalsdecomposition:basicsignalCHAPTERLinearTime- ResponseResponsesynthesis:basicresponse(impulse2.2.LinearTime-InvariantSystems(WHYAbasicfact:IfweknowtheresponseofanLTItosomeinputs,weactuallyknowtheresponsetomanyKeypointsofSignalsdecomposition:basicsignalResponseResponsesynthesis:basicresponse(impulse2.12.1Discrete-timeLTIsystem:TheconvolutionTheRepresentationofDiscrete-timeSignalsinTermsofImpulses2.12.1Discrete-timeLTIsystem:TheconvolutionTheRepresentationofDiscrete-timeSignalsinTermsofImpulses.1Decompositionofadiscrete-timesignalintoaweightedsumofshifted/.1Decompositionofadiscrete-timesignalintoaweightedsumofshiftedIfx[n]=u[n],x[n][nkkx[n]x[k][nk]k
TheDiscrete-timeUnitImpulseResponseandtheConvolutionSumRepresentationofLTISystems(1)UnitImpulse(Sample)UnitUnitimpulseTheDiscrete-timeUnitImpulseResponseandtheConvolutionSumRepresentationofLTISystemsUnitImpulse(Sample)UnitUnitimpulseGivenGiventheUnitImpulseResponse:
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Notu[n]1
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h(t- a2.32.3PropertiesofLTIOutputResponseofLTIy[n]=x[n]y[n]=x[n]y[n]x[n]*h[n]x[k]h[nk y(t)=x(t)Convolution forLTIsItsoutputresponsecanbeobtainedbyconvolutionItcanbecompletelycharacterizedbyitsUnitImpulsey(t)
x(t)*h(t)
2.3.12.3.1TheCommutativeDiscrete x[n]*h[n]=h[n]Continuoustime:x(t)*h(t) h(t)Howtoh y(t)=x(t)hy(t)=h(t)Example2.7(Calculateitbyyourself!)(SimilartoExample2.4)Example2.8(Calculateitbyyourself!)(SimilartoExample2.5)ExperimentExperimentdemonstrationforConvolutionIntegralTheDistributiveProDiscretex[n]*{h1[n]+h2[n]}=x[n]*h1[n]+x[n]*h2[n]Continuoustime:x(t)*{h1(t)+h2(t)}=x(t)*h1(t)+x(t)y(t)=x(t)y(t)=x(t)y(t)=x(t)y(t)=x(t)*h1(t)+x(t)x(Input:SumofunitOutut:Sumofunitimulseres x[n]=
LTIy[n]=
x(t)
y[n]=x[n]*ConvolutionIy(t)=+¥x(t)h(t-y(t)=x(t)*ConvolutionExampleExamplex[n]
(12
u[n]
2nu[n],h[n]
n )u[n]n
x2[n]
y[n]
(1[n
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Independentvariablereplace: fix[k],h[n] fih[k]TimeInversalTimeShift: fih[-k] fih[n-k] + Four
fix(t),h(t) Transformationoftinh(t): fih(-t) fih(t- x(t)h(t-t)TheAssociativeProDiscretex[n]*{h1[n]
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Howy(t)=x(t)*{h1(t)y(t)=x(t)*h1(t)h1(t)
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LTIsstemwithandwithoutMemorylessDiscretetime: y[n]=kx[n],h[n]=k[n]Continuoustime:y(t)=kx(t),h(t)=k(t)x(t)y(x(t)y(t)kx(t)x(t)k(t)(t)kImplythat:x(t)*(t)=x(t)andx[n]
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x[n]k
(t)*h(t)=h(tAd[n-k]*x[n]=Ax[n-k(t-)*h(t)=h(t-C-C-
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x(t)
x(t)(t)x(t)(t)So,fortheinvertibleh(t)*h1(t)=(t)orh[n]d(t-t)*x(t)=x(t-t)h(t)h(t)(tt0
t0yy(t)x(t) (tt0)x(tt0
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h1(t)
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ConvergenceConvergenceofu[n]*u[-n]= Notu[n]*1=u[n]*{u[n-1]+u[-n]}=?Notu(t)*u(-t)=?Notu(t)*1=u(t)*{u(t)+u(-t)}=?Not
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2.32.3PropertiesofLTIOutputResponseofLTI y(t)=x(t)Convolutiony(t)=x(t)*h(t)=-¥x(t)h(t-
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2.3.12.3.1TheCommutative x[n]*h[n]=h[n]Continuoustime:x(t)*h(t) h(t)Howto y(t)=x(t)y(t)=h(t)x[k]u[nx[k]u[nk]kDiscretetimesystemsatisfythecondition:h[n]=0forn<0Continuoustimesystemsatisfythecondition:y[n] h[n]=0for TheDistributiveProDiscretex[n]*{h1[n]+h2[n]}=x[n]*h1[n]+x[n]*h2[n]Continuoustime:x(t)*{h1(t)+h2(t)}=x(t)*h1(t)+x(t)Howtoxxh1y(t)=x(t)*h1(t)+x(t)2.3.72.3.7StabilityforLTIDefinitionofstability:Everyboundedinputproducesaboundedoutput.Discretetime y[n]
k
x[
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x[n
k]h[k |y[n]
|
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)
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)|
|y(t)|If|x(t)|<B,theconditionfor|y(t)|<A|h()|disTheAssociativeProDiscretex[n]*{h1[n]*h2[n]}={x[n]*h1[n]}Continuousx(t)*{1(t)*2(t)}={x(t)*1(t)}*HowtoProveh1(t)y(t)=x(t)*{h1(t)y(t)=x(t)*h1(t)ExampleExamplePuretimeshift y[n]
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h(t)
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h(t)=k|h[k]|k|h()|d
h[n]=0forn<0h[n]*h1[n]=[n]
h(t)=0fort<0h(t)*h1(t)=(t)2.3.52.3.5ofLTIsOriginalsystem:Reversesystem: x(t)
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Identityht=
x[n][n]
y(t)
x(t)(t)
Gain y[n]
x[n]K[n]
y(t)
x(t)
(t)
yy[n]x[n][nn]x[nn00y(t)x(t)(tt)x(tt y(t)=x(t-t0yy(t)=x(t)*(t-t0)=x(t-t0 h(t)*h1(t)= =d(t-t0)*1t= =y[n]
x[n]h[n]
x[knkn
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x(t)h(t)
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'(t)][h(t)
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h[n]=0forhh(t)u(t)u(th(t)1x(t)110t1t101
y(t)
x( )d2.3.72.3.7StabilityforLTIDefinitionofstability:Everyboundedinputproducesaboundedoutput.Discretetime y[n]=x[k]h[n-k],or,x[n-k]h[kk=- k=-
|y[n]
|
x[n-k]||h[k]|<B
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y(t)x(t)h(t)y(kt)x(kt)h(t)y(tt0)x(tt0)h(t)x(t)h(t)y(tx(tt)h(tt)y(t2t000x(kt)h(kt)1y(ktk
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Determinethesystemresponse(output)recursivelyTypicalLTISystemanditsUnitImpulseDiscrete ContinuousIdentity ht=d Gain y(t)=x(t)* (t)=Time sConditionConditionoInitialResty[n]=x[n]*h[n]=x[k
y(t)=x(t)*h(t)=
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h[k]=h(-1)[n],tth()dt=h-1(t), h(t)=ss(t)=-n
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Proofxt)h(t)=x(t)*h(t)*u(t)*d'=[x(t)*'(t)]*[h(t)*u(t)]'=x(t)h(1)(t)'HowHowtogettheHomogeneous y(t)SolvethehomogeneousdifferentialNk Nk
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y(N1)(t)21Systemyy(t)y(t)y*(t)NaturalForcedGeneralresponse=Zero-state+Zero-Input=Generalresponse=Zero-state+Zero-Input=Forced+NaturalConvolutionConvolution.. .. BlockDiagramRepresentationsofFirst-orderSystemsDescribedbyDifferentialandDifferenceEquationDiscretetimesystem:DifferenceNNk
bkMkM
BasicelementsforadiscretetimesystemAnMultiplicationbyaC.Anunit ConvolutionBasicMultiplicationMultiplicationbyaaUnitDiscrete Continuous
y(-t)?x(-t)*h(t)y(kt)?x(kt)*h(t)y(t-t0)=x(t-t0)x(-t)*h(-t)=y(-tx(kt)*h(kt)=1y(ktk x(t-t)*h(t-t)=y(t-2t) y[n]+ay[n-KeKewordsforChaterContinuoustime d
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dtkBasicAnMultiplicationbyaAn(differentiator)CausalLTISystemsDescribedbyLinearConstant-CoefficientDifferentialandDifferenceDiscretetimesystem:Difference k
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k
RecursiveaNy[n-N]+aN-1y[n-(N-1)]+L+a1y[n-1]+a0 (M-1)]+L+ initialReviewReviewforChaptersystemUnitImpulseConvolutionLTIsystemDescribedbyLinearConstant-CoefficientDifferenceandDifferentialEquation(LCCDE)
(ConditionofInitialBlockDiagramy[-1],……,y[-(N- (NvaluesReviewfory[n]=x[n]Reviewfory[n]=x[n]y(t)=x(t)x[n]x[n]x[k][nkkResponsey[n]x[k]h[nkx(t)x()(ty(t)x()h(tk
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k
x(t)
y(t)
x(t)*h(t)y[n]
x[n]* (t1,t2
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(t3,t4
[N3
N4
(t1t3,
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Determinethesystemresponse(output)recursivelyPropertiesPropertiesofLTIDiscrete Continuous(1)x[n]*h[n]=h[n] x(t)*h(t) h(t)2xn*{h1n+h2 x(t)
h(t)=k |h[k]|k|h()|d
h[n]=0forn<0h[n]*h1[n]=[n]
h(t)=0fort<0h(t)*h1(t)=(t)ConditionConditionoInitialRestSolutionSolutionforDifferenceRecursiveequationyy[n]x[n]1y[n2Initialcondition
=(ConditionofInitial x(n)
n
y(n)
n(ConditionofInitialx(n)=n<y(n)=x(n)=n<y(n)=n<Supposesystemiscausal.Showthat(*)Suppose(*)holds.ShowthatthesystemisHomeworkHomeworkforChapterPartI(卷積和2.3,2.5,PartII(卷積積分):2.10(a),Part 2.1,2.25(b),PartIV(差分方程求解 , , 入理解ExampleExampleRecursiveequationInitialcondition
2
y[n-(LTI, x(n)= n<(ConditionofInitial y(n)= n<n< y(n)=n=0 n=1
y[0]=121n= 2M
1)222 2
nn1 y[n]=()n2
Theimpulse
2
nfiniteImpulseResponse Recursive k
k
InfiniteImpulseResponse M x[n-kMk=0FiniteImpulseResponse2.4.22.4.2LinearConstant-CoefficientDifferentialcofficientdiffrential dk
dx
=dtdt
dtkaNy(N)(t)+ y(N-1)(t)+L+a1y'(t)+a0=bMx(M)(t)+ x(M-1)(t)+L+bx'(t)+b0 andinitialy(t0),y’(t0),……,y(N- (NvaluesSolutionoftheN-thorderlin
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