信号与系统分析2022春季作业-参考答案:第四次作业-第一部分

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§00 业题目

  作业要求链接: 信号与系统2022春季学期第四次作业

§01 考答案


1.1 求解卷积运算

1.1.1 求解两个信号卷积

 Ⅰ.第一小题

  求解: 根据 u ( t ) u\left( t \right) u(t)卷积特性: u ( t ) ∗ f ( t ) = ∫ − ∞ t f ( τ ) d τ u\left( t \right) * f\left( t \right) = \int_{ - \infty }^t {f\left( \tau \right)d\tau } u(t)f(t)=tf(τ)dτ。所以:

f ( t ) = u ( t ) ∗ e − 3 t ⋅ u ( t ) = ∫ 0 t e − 3 τ d τ = − 1 3 e − 3 t ∣ 0 t = 1 3 ( 1 − e − 3 t ) f\left( t \right) = u\left( t \right) * e^{ - 3t} \cdot u\left( t \right) = \int_0^t {e^{ - 3\tau } d\tau } = \left. { { { - 1} \over 3}e^{ - 3t} } \right|_0^t = {1 \over 3}\left( {1 - e^{ - 3t} } \right) f(t)=u(t)e3tu(t)=0te3τdτ=31e3t0t=31(1e3t)

信号的波形:
▲ 信号f(t)的波形

▲ 信号f(t)的波形

 Ⅱ.第四小题

  求解: 根据 δ ( t ) \delta \left( t \right) δ(t)以下两个性质:

  • 偶对称: δ ( t ) = δ ( − t ) \delta \left( t \right) = \delta \left( { - t} \right) δ(t)=δ(t)
  • 卷积特性: f ( t ) ∗ δ ( t − t 0 ) = f ( t − t 0 ) f\left( t \right) * \delta \left( {t - t_0 } \right) = f\left( {t - t_0 } \right) f(t)δ(tt0)=f(tt0)

  可以进行如下化简:

f ( t ) = t [ u ( t ) − u ( t − 2 ) ] ∗ δ ( 2 − t ) f\left( t \right) = t\left[ {u\left( t \right) - u\left( {t - 2} \right)} \right] * \delta \left( {2 - t} \right) f(t)=t[u(t)u(t2)]δ(2t) = t [ u ( t ) − u ( t − 2 ) ] ∗ δ ( t − 2 ) = t\left[ {u\left( t \right) - u\left( {t - 2} \right)} \right] * \delta \left( {t - 2} \right) =t[u(t)u(t2)]δ(t2) = ( t − 2 ) ⋅ [ u ( t − 2 ) − u ( t − 4 ) ] = \left( {t - 2} \right) \cdot \left[ {u\left( {t - 2} \right) - u\left( {t - 4} \right)} \right] =(t2)[u(t2)u(t4)]

信号的波形:
▲ 信号f(t)的波形

▲ 信号f(t)的波形


 Ⅲ.第七小题

  求解: 原来信号可以看成两个信号的卷积: f ( t ) = f 1 ( t ) ∗ f 2 ( t ) f\left( t \right) = f_1 \left( t \right) * f_2 \left( t \right) f(t)=f1(t)f2(t)

  • f 1 ( t ) = [ ( t + 2 ) u ( t + 2 ) − 2 t ⋅ u ( t ) + ( t − 2 ) ⋅ u ( t − 2 ) ] f_1 \left( t \right) = \left[ {\left( {t + 2} \right)u\left( {t + 2} \right) - 2t \cdot u\left( t \right) + \left( {t - 2} \right) \cdot u\left( {t - 2} \right)} \right] f1(t)=[(t+2)u(t+2)2tu(t)+(t2)u(t2)]
  • f 2 ( t ) = [ δ ′ ( t + 2 ) − δ ′ ( t − 2 ) ] f_2 \left( t \right) = \left[ {\delta '\left( {t + 2} \right) - \delta '\left( {t - 2} \right)} \right] f2(t)=[δ(t+2)δ(t2)]

f 1 ( t ) f_1 \left( t \right) f1(t)可以分解成两端组成:
f 1 ( t ) = ( t + 2 ) ⋅ u ( t + 2 ) − [ ( t + 2 ) + ( t − 2 ) ] ⋅ u ( t ) + ( t − 2 ) ⋅ u ( t − 2 ) f_1 \left( t \right) = \left( {t + 2} \right) \cdot u\left( {t + 2} \right) - \left[ {\left( {t + 2} \right) + \left( {t - 2} \right)} \right] \cdot u\left( t \right) + \left( {t - 2} \right) \cdot u\left( {t - 2} \right) f1(t)=(t+2)u(t+2)[(t+2)+(t2)]u(t)+(t2)u(t2) = ( t + 2 ) ⋅ [ u ( t + 2 ) − u ( t ) ] − ( t − 2 ) ⋅ [ u ( t ) − u ( t − 2 ) ] = \left( {t + 2} \right) \cdot \left[ {u\left( {t + 2} \right) - u\left( t \right)} \right] - \left( {t - 2} \right) \cdot \left[ {u\left( t \right) - u\left( {t - 2} \right)} \right] =(t+2)[u(t+2)u(t)](t2)[u(t)u(t2)]

  它实际上是一个中心位于0点的对称等腰三角形:

▲ f1(t)的波形

▲ f1(t)的波形

  它的导数为:

f 1 ′ ( t ) = [ u ( t + 2 ) − u ( t ) ] − [ u ( t ) − u ( t − 2 ) ] f_1 '\left( t \right) = \left[ {u\left( {t + 2} \right) - u\left( t \right)} \right] - \left[ {u\left( t \right) - u\left( {t - 2} \right)} \right] f1(t)=[u(t+2)u(t)][u(t)u(t2)] = u ( t + 2 ) − 2 u ( t ) + u ( t − 2 ) = u\left( {t + 2} \right) - 2u\left( t \right) + u\left( {t - 2} \right) =u(t+2)2u(t)+u(t2)

▲ f1'(t)的波形

▲ f1'(t)的波形

根据

  • δ ′ ( t ) \delta '\left( t \right) δ(t)的卷积特性: f ( t ) ∗ δ ′ ( t ) = f ′ ( t ) f\left( t \right) * \delta '\left( t \right) = f'\left( t \right) f(t)δ(t)=f(t)
  • 卷积延迟特性: f ( t ) ∗ δ ′ ( t − t 0 ) = f ′ ( t − t 0 ) f\left( t \right) * \delta '\left( {t - t_0 } \right) = f'\left( {t - t_0 } \right) f(t)δ(tt0)=f(tt0)

  所以计算 f 1 ( t ) ∗ f 2 ( t ) f_1 \left( t \right) * f_2 \left( t \right) f1(t)f2(t)的结果就等于:
f ( t ) = f 1 ′ ( t + 2 ) − f 1 ′ ( t − 2 ) f\left( t \right) = f_1 '\left( {t + 2} \right) - f_1 '\left( {t - 2} \right) f(t)=f1(t+2)f1(t2) = u ( t + 4 ) − 2 u ( t + 2 ) + 2 u ( t ) − 2 u ( t − 2 ) + u ( t − 4 ) = u\left( {t + 4} \right) - 2u\left( {t + 2} \right) + 2u\left( t \right) - 2u\left( {t - 2} \right) + u\left( {t - 4} \right) =u(t+4)2u(t+2)+2u(t)2u(t2)+u(t4)

▲ f(t)的波形

▲ f(t)的波形

1.1.2 图解法求解卷积

  求解:

f 1 ( t ) , f 2 ( t ) f_1 \left( t \right),f_2 \left( t \right) f1(t),f2(t)的波形如下图所示:


  令 f 1 u ( t ) = f 1 ( t ) ∗ u ( t ) f_{1u} \left( t \right) = f_1 \left( t \right) * u\left( t \right) f1u(t)=f1(t)u(t),那么 f 1 u ( t − 2 ) = f 1 ( t ) ∗ u ( t − 2 ) f_{1u} \left( {t - 2} \right) = f_1 \left( t \right) * u\left( {t - 2} \right) f1u(t2)=f1(t)u(t2)。所以:

f 1 ( t ) ∗ f 2 ( t ) = f 1 ( t ) ∗ [ u ( t ) − u ( t − 2 ) ] = f 1 u ( t ) − f 1 u ( t − 2 ) f_1 \left( t \right) * f_2 \left( t \right) = f_1 \left( t \right) * \left[ {u\left( t \right) - u\left( {t - 2} \right)} \right] = f_{1u} \left( t \right) - f_{1u} \left( {t - 2} \right) f1(t)f2(t)=f1(t)[u(t)u(t2)]=f1u(t)f1u(t2)

  下面首先计算 f 1 u ( t ) f_{1u} \left( t \right) f1u(t)

f 1 u ( t ) = e − 1 2 t ∗ u ( t ) = ∫ − ∞ t e − τ 2 d τ = − 2 e − t 2 ∣ 0 t = 2 ( 1 − e − t 2 ) f_{1u} \left( t \right) = e^{ - {1 \over 2}t} * u\left( t \right) = \int_{ - \infty }^t {e^{ - {\tau \over 2}} d\tau } = \left. { - 2e^{ - {t \over 2}} } \right|_0^t = 2\left( {1 - e^{ - {t \over 2}} } \right) f1u(t)=e21tu(t)=te2τdτ=2e2t0t=2(1e2t)


  那么: f 1 ( t ) ∗ [ u ( t ) − u ( t − 2 ) ] = 2 ( 1 − e − t 2 ) ⋅ u ( t ) − 2 ( 1 − e − t − 2 2 ) ⋅ u ( t − 2 ) f_1 \left( t \right) * \left[ {u\left( t \right) - u\left( {t - 2} \right)} \right] = 2\left( {1 - e^{ - {t \over 2}} } \right) \cdot u\left( t \right) - 2\left( {1 - e^{ - { {t - 2} \over 2}} } \right) \cdot u\left( {t - 2} \right) f1(t)[u(t)u(t2)]=2(1e2t)u(t)2(1e2t2)u(t2)

1.1.3 使用Python绘制

  下面使用Python绘制的 f 1 ( t ) , f 2 ( t ) f_1 \left( t \right),f_2 \left( t \right) f1(t),f2(t)波形:
▲ f1(t),f2(t) 波形

▲ f1(t),f2(t) 波形

1.1.4 利用卷积求解系统响应

(1)必做题

  题中参与卷积的序列都是短的信号,可以使用竖式方法进行手工计算。下面给出使用python进行数值计算的结果,以供大家对照检查。

y [ n ] = [ 0 , 0 , 1 , 4 , 6 n = 0 , 4 , 1 , 0 , 0 ] y\left[ n \right] = \left[ {0,0,1,4,6_{n = 0} ,4,1,0,0} \right] y[n]=[0,0,1,4,6n=0,4,1,0,0]

(2)选做题

y [ n ] = [ 0 , 0 , 1 , 3 , 1 , 1 , 3 n = 0 , 3 , 1 , 1 , 2 , 0 , 0 ] y\left[ n \right] = \left[ {0,0,1,3,1,1,3_{n = 0} ,3,1,1,2,0,0} \right] y[n]=[0,0,1,3,1,1,3n=0,3,1,1,2,0,0]

  • 确定n=0点位置:

  使用MATLAB的CONV命令进行求解的时候需要注意到对应的序列的原点对应的位置。

  计算绘制离散数据卷积python程序核心代码段:

x = [0, 1, 2, 1, 0]
h = [0, 1, 2, 1, 0]

xch = convolve(x, h)
xtime = linspace(-2, len(xch) - 2, \
            len(xch), endpoint=False)

printf(xch)
printf(xtime)

plt.stem(xtime, xch, basefmt='C1-')
plt.xlabel('n')
plt.ylabel('x[n]*h[n]')
plt.grid(axis='y')
plt.show()

1.1.5 求解卷积数值

  求解: 使用图解方法帮助确定积分上下限,选择 f 1 ( t ) f_1 \left( t \right) f1(t)进行反褶:

  (1) f ( 1 ) f\left( 1 \right) f(1)

  下面是对应的函数平移和重叠的情况,

f ( 1 ) = ∫ − ∞ ∞ f 1 ( 1 − τ ) ⋅ f 2 ( τ ) d τ f\left( 1 \right) = \int_{ - \infty }^\infty {f_1 \left( {1 - \tau } \right) \cdot f_2 \left( \tau \right)d\tau } f(1)=f1(1τ)f2(τ)dτ

  卷积结果为:
f ( 1 ) = 2 × 2 2 × 2 = 4 f\left( 1 \right) = { {2 \times 2} \over 2} \times 2 = 4 f(1)=22×2×2=4

  (2) f ( 3 ) f\left( 3 \right) f(3)

  下面是对应的函数平移和重叠的情况:

f ( 3 ) = ∫ − ∞ ∞ f 1 ( 3 − τ ) ⋅ f 2 ( τ ) d τ f\left( 3 \right) = \int_{ - \infty }^\infty {f_1 \left( {3 - \tau } \right) \cdot f_2 \left( \tau \right)d\tau } f(3)=f1(3τ)f2(τ)dτ


  卷积结果为:
f ( 3 ) = 1 + 2 2 × 1 × 2 − 1 × 1 2 = 2 1 2 f\left( 3 \right) = { {1 + 2} \over 2} \times 1 \times 2 - { {1 \times 1} \over 2} = 2{1 \over 2} f(3)=21+2×1×221×1=221

  (5) f ( 5 ) f\left( 5 \right) f(5)

  下面是对应的函数平移和重叠的情况:
f ( 5 ) = ∫ − ∞ ∞ f 1 ( 5 − τ ) ⋅ f 2 ( τ ) d τ f\left( 5 \right) = \int_{ - \infty }^\infty {f_1 \left( {5 - \tau } \right) \cdot f_2 \left( \tau \right)d\tau } f(5)=f1(5τ)f2(τ)dτ

  卷积结果为:
f ( 5 ) = 1 + 2 2 × 1 × ( − 1 ) = − 3 2 f\left( 5 \right) = { {1 + 2} \over 2} \times 1 \times \left( { - 1} \right) = - {3 \over 2} f(5)=21+2×1×(1)=23

1.1.6 ⊙ 绘制卷积信号波形

▲ f1(t)*f2(t)波形

▲ f1(t)*f2(t)波形

#!/usr/local/bin/python
# -*- coding: gbk -*-
#============================================================
# DRAW2.PY                     -- by Dr. ZhuoQing 2020-03-29
#
# Note:
#============================================================

from headm import *

f1 = linspace(0, 2, 200, endpoint=False)
f2 = concatenate((linspace(2, 2, 400, endpoint=False),\
                  linspace(-1, -1, 200)), axis=0)

fconv = convolve(f1, f2) * 0.01
tdata = linspace(-2, 6, len(fconv))

plt.plot(tdata, fconv)
plt.grid(True)
plt.xlabel('t')
plt.ylabel('f1*f2')
plt.show()

#------------------------------------------------------------
#        END OF FILE : DRAW2.PY
#============================================================

1.1.7 求解卷积信号

  求解:

  使用图解方法辅助求解 x ( t ) , h ( t ) x\left( t \right),h\left( t \right) x(t),h(t)的卷积。

  (1) t ≤ 3 t \le 3 t3

x ( t ) ∗ h ( t ) = 0 x\left( t \right) * h\left( t \right) = 0 x(t)h(t)=0

  (2) 3 < t < 5 3 < t < 5 3<t<5

x ( t ) ∗ h ( t ) = ∫ 0 t − 3 e − 3 τ d τ = − 1 3 e − 3 τ ∣ 0 t − 3   = 1 3 ( 1 − e − 3 ( t − 3 ) ) x\left( t \right) * h\left( t \right) = \int_0^{t - 3} {e^{ - 3\tau } d\tau } = { { - 1} \over 3}\left. {e^{ - 3\tau } } \right|_0^{t - 3} \, = {1 \over 3}\left( {1 - e^{ - 3\left( {t - 3} \right)} } \right) x(t)h(t)=0t3e3τdτ=31e3τ0t3=31(1e3(t3))

  (3) t > 5 t > 5 t>5
x ( t ) ∗ h ( t ) = ∫ t − 5 t − 3 e − 3 τ d τ = − 1 3 e − 3 τ ∣ t − 5 t − 3 x\left( t \right) * h\left( t \right) = \int_{t - 5}^{t - 3} {e^{ - 3\tau } d\tau } = { { - 1} \over 3}\left. {e^{ - 3\tau } } \right|_{t - 5}^{t - 3} x(t)h(t)=t5t3e3τdτ=31e3τt5t3 = 1 3 ( e − 3 ( t − 5 ) − e − 3 ( t − 3 ) )    = e 15 − e 9 3 e − 3 t = {1 \over 3}\left( {e^{ - 3\left( {t - 5} \right)} - e^{ - 3\left( {t - 3} \right)} } \right)\,\, = { {e^{15} - e^9 } \over 3}e^{ - 3t} =31(e3(t5)e3(t3))=3e15e9e3t

  根据卷积微分性质:

y ′ ( t ) = x ( t ) ∗ h ′ ( t )   = x ( t ) ∗ [ δ ( t − 3 ) − δ ( t − 5 ) ] y'\left( t \right) = x\left( t \right) * h'\left( t \right)\, = x\left( t \right) * \left[ {\delta \left( {t - 3} \right) - \delta \left( {t - 5} \right)} \right] y(t)=x(t)h(t)=x(t)[δ(t3)δ(t5)] = x ( t − 3 ) − x ( t − 5 )   = e − 3 ( t − 3 ) ⋅ u ( t − 3 ) − e − 3 ( t − 5 ) ⋅ u ( t − 5 ) = x\left( {t - 3} \right) - x\left( {t - 5} \right)\, = e^{ - 3\left( {t - 3} \right)} \cdot u\left( {t - 3} \right) - e^{ - 3\left( {t - 5} \right)} \cdot u\left( {t - 5} \right) =x(t3)x(t5)=e3(t3)u(t3)e3(t5)u(t5)


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转载自blog.csdn.net/zhuoqingjoking97298/article/details/124272342