狄拉克函数其实是一种广义函数,有关广义函数的更多内容,可以参考施瓦兹大佬亲笔写的《广义函数论》,很精彩。
定义由狄拉克给出:
∫ − ∞ ∞ δ ( t ) d t = 1 \int_{-\infty}^{\infty} \delta(t)\, dt = 1∫−∞∞δ(t)dt=1 δ ( t ) = 0 , t ≠ 0 \delta(t) = 0 , t \ne 0δ(t)=0,t=0
δ ( t ) \delta(t)δ(t)的基本性质
- 筛选性
- x ( t ) δ ( t − t 0 ) = x ( t 0 ) δ ( t − t 0 ) x(t)\delta(t-t_0) = x(t_0)\delta(t-t_0)x(t)δ(t−t0)=x(t0)δ(t−t0)
- x ( t ) δ ( t ) = x ( 0 ) δ ( t ) x(t)\delta(t) = x(0)\delta(t)x(t)δ(t)=x(0)δ(t)
- ∫ − ∞ ∞ x ( t ) δ ( t − t 0 ) d t = x ( t 0 ) \int_{-\infty}^{\infty}x(t)\delta(t-t_0)\, dt = x(t_0)∫−∞∞x(t)δ(t−t0)dt=x(t0)
- ∫ − ∞ ∞ x ( t ) δ ( t ) d t = x ( 0 ) \int_{-\infty}^{\infty}x(t)\delta(t)\, dt = x(0)∫−∞∞x(t)δ(t)dt=x(0)
- 偶函数:δ ( − t ) = δ ( t ) \delta(-t) = \delta(t)δ(−t)=δ(t)
- 尺度变换:δ ( a t ) = 1 ∣ a ∣ δ ( t ) \delta(at) = \frac{1}{\vert a \vert}\delta(t)δ(at)=∣a∣1δ(t)
- 卷积特性:x ( t ) ∗ δ ( t − t 0 ) = x ( t − t 0 ) x(t) * \delta(t-t_0) = x(t-t_0)x(t)∗δ(t−t0)=x(t−t0)
- 对任意函数f ( t ) f(t)f(t),都有δ ( f ( t ) ) = ∑ i 1 ∣ f ′ ( t i ) ∣ δ ( t − t i ) \delta(f(t)) = \sum_i\frac{1}{\vert f^\prime(t_i) \vert}\delta(t-t_i)δ(f(t))=i∑∣f′(ti)∣1δ(t−ti)其中t i t_iti为f ( t ) f(t)f(t)的零点。
- 与阶跃函数u ( t ) u(t)u(t)的关系:∫ ∞ t δ ( τ ) d τ = u ( t ) \int_{\infty}^t \delta(\tau)\,d\tau= u(t)∫∞tδ(τ)dτ=u(t) d d t u ( t ) = δ ( t ) \frac{d}{dt}u(t) = \delta(t)dtdu(t)=δ(t)
注意,同一时刻出现的单位冲激、高阶冲激(二阶导以上的)间的乘积,如δ 2 ( t ) \delta^2(t)δ2(t),δ ( t ) δ ′ ( t ) \delta(t)\delta^\prime(t)δ(t)δ′(t)等,都没有意义。
δ ′ ( t ) \delta^\prime(t)δ′(t)的基本性质
- δ ′ ( t ) \delta^\prime(t)δ′(t)的面积为0:∫ − ∞ ∞ δ ′ ( t ) d t = 0 \int_{-\infty}^{\infty} \delta^\prime(t)\, dt = 0∫−∞∞δ′(t)dt=0
- 筛选性:
- x ( t ) δ ′ ( t ) = x ( 0 ) δ ′ ( t ) − x ′ ( 0 ) δ ( t ) x(t)\delta^\prime(t) = x(0)\delta^\prime(t) - x^\prime(0)\delta(t)x(t)δ′(t)=x(0)δ′(t)−x′(0)δ(t)
- ∫ − ∞ ∞ x ( t ) δ ′ ( t ) d t = − x ′ ( 0 ) \int_{-\infty}^{\infty}x(t)\delta^\prime(t)\, dt = -x^\prime(0)∫−∞∞x(t)δ′(t)dt=−x′(0)
- ∫ − ∞ ∞ x ( t ) δ ′ ( t − t 0 ) d t = − x ′ ( t 0 ) \int_{-\infty}^{\infty}x(t)\delta^\prime(t - t_0)\, dt = -x^\prime(t_0)∫−∞∞x(t)δ′(t−t0)dt=−x′(t0)
- 奇函数:δ ′ ( − t ) = − δ ′ ( t ) \delta^\prime(-t) = -\delta^\prime(t)δ′(−t)=−δ′(t)
- 尺度变换:δ ′ ( a t ) = δ ′ ( t ) a ∣ a ∣ \delta^\prime(at) = \frac{\delta^\prime(t)}{a\vert a \vert}δ′(at)=a∣a∣δ′(t)
δ ( k ) ( t ) \delta^{(k)}(t)δ(k)(t)的基本性质
这一条是上面两条的推广,当阶次提高到k kk,性质如下(δ ( k ) ( t ) \delta^{(k)}(t)δ(k)(t)表示δ ( t ) \delta(t)δ(t)的k kk阶导数):
- 筛选性:∫ − ∞ ∞ δ ( k ) ( t ) x ( t ) d t = ( − 1 ) k x k ( 0 ) , k ≥ 0 \int_{-\infty}^{\infty}\delta^{(k)}(t)x(t)\, dt = (-1)^kx^k(0), k\geq0∫−∞∞δ(k)(t)x(t)dt=(−1)kxk(0),k≥0
- 奇偶性:δ ( k ) ( t ) = ( − 1 ) k δ ( k ) ( − t ) \delta^{(k)}(t) = (-1)^k\delta^{(k)}(-t)δ(k)(t)=(−1)kδ(k)(−t),这表明,若k kk为奇数,则δ ( k ) ( t ) \delta^{(k)}(t)δ(k)(t)为奇函数,否则为偶函数。
- ∫ − ∞ ∞ δ ( k ) ( t ) d t = 0 , k ≥ 1 \int_{-\infty}^{\infty}\delta^{(k)}(t)\, dt = 0, k\geq1∫−∞∞δ(k)(t)dt=0,k≥1
- 若x ( t ) x(t)x(t)的k kk阶导数在t = 0 t=0t=0处连续,则x ( t ) δ ( k ) ( t ) = ∑ m = 0 k ( − 1 ) m C k m x ( m ) ( 0 ) δ ( k − m ) ( t ) , k ≥ 0 x(t)\delta^{(k)}(t) = \sum_{m=0}^k(-1)^mC_k^mx^{(m)}(0)\delta^{(k-m)}(t), k\geq0x(t)δ(k)(t)=m=0∑k(−1)mCkmx(m)(0)δ(k−m)(t),k≥0
- x ( t ) ∗ δ ( k ) ( t − t 0 ) = x ( k ) ( t − t 0 ) x(t)*\delta^{(k)}(t-t_0) = x^{(k)}(t-t_0)x(t)∗δ(k)(t−t0)=x(k)(t−t0),当k = − 1 k=-1k=−1时,就变成了x ( t ) ∗ u ( t ) = ∫ − ∞ t x ( τ ) d τ x(t)*u(t) = \int_{-\infty}^tx(\tau)\,d\taux(t)∗u(t)=∫−∞tx(τ)dτ
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