Discrete Time Random Processes

Berkeley Notes
Home Resources
Search…
Introduction
Filtering
EE222
Discrete Time Random Processes
Definition 4
A Discrete-Time Random Process is a countably infinite collection of random variables on the same probability space {Xn:n∈Z}\{X_n: n\in\mathbb{Z}\}{Xn​:n∈Z}
.
Discrete Time Random Processes have a mean function μn=E[Xn]\mu_n = \mathbb{E}\left[X_n\right] μn​=E[Xn​]
and an auto-correlation function RX(n1,n2)=E[Xn1Xn2∗]R_X(n_1, n_2) = \mathbb{E}\left[X_{n_1}X_{n_2}^*\right]RX​(n1​,n2​)=E[Xn1​​Xn2​∗​]
​
Wide-Sense Stationary Random Processes
Definition 5
A Wide-Sense Stationary Random Process is a disrete-time random process with constant mean, finite variance, and an autocorrelation function that can be re-written to only depend on n1−n2n_1-n_2n1​−n2​
.
We call this wide-sense stationary because the mean and covariance do not change as the process evolves. In a strict-sense stationary process, the distribution of each random variable in the process would not change.
Definition 6
A WSS process Z∼WN(0,σ2)Z\sim \mathcal{WN}(0, \sigma^2)Z∼WN(0,σ2)
 is a white noise process with variance σ2\sigma^2σ2
 if and only if E[Zn]=0\mathbb{E}\left[Z_n\right] = 0E[Zn​]=0
 and E[ZnZm∗]=σ2δ[n,m]\mathbb{E}\left[Z_nZ_m^*\right] = \sigma^2\delta[n, m]E[Zn​Zm∗​]=σ2δ[n,m]
.
Spectral Density
Recall that the Discrete Time Fourier Transform is given by
​X(ejω)=∑n=−∞∞x[n]e−jωn.X(e^{j\omega}) = \sum_{n=-\infty}^{\infty}x[n]e^{-j\omega n}.X(ejω)=∑n=−∞∞​x[n]e−jωn.
​
The Inverse Discrete Time Fourier Transform is given by
​x[n]=12π∫−ππX(ejω)ejωndω.x[n] = \frac{1}{2\pi}\int_{-\pi}^{\pi}X(e^{j\omega})e^{j\omega n}d\omega.x[n]=2π1​∫−ππ​X(ejω)ejωndω.
​
Since the DTFT is an infinite summation, it may or may not converge.
Definition 7
A signal x[n]x[n]x[n]
 belongs to the l1l^1l1
 class of signals if the series converges absolutely. In other words,
​∑k=−∞∞∣x[k]∣<∞.\sum_{k=-\infty}^{\infty}|x[k]| < \infty.∑k=−∞∞​∣x[k]∣<∞.
​
This class covers most real-world signals.
Theorem 5
If x[n]x[n]x[n]
 is a l1l^1l1
 signal, then the DTFT X(ejω)X(e^{j\omega})X(ejω)
 converges uniformly and is well-defined for every ω\omegaω
. X(ejω)X(e^{j\omega})X(ejω)
is also a continuous function.
Definition 8
A signal x[n]x[n]x[n]
 belongs to the l2l^2l2
 class of signals if it is square summable. In other words,
​∑k=−∞∞∣x[k]∣2<∞.\sum_{k=-\infty}^{\infty}|x[k]|^2 < \infty.∑k=−∞∞​∣x[k]∣2<∞.
​
The l2l^2l2
 class contains important functions such as sinc\text{sinc}sinc
.
Theorem 6
If x[n]x[n]x[n]
 is a l2l^2l2
 signal, then the DTFT X(ejω)X(e^{j\omega})X(ejω)
 is defined almost everywhere and only converges in the mean-squared sense:
​lim⁡N→∞∫−ππ∣(∑k=−NNx[k]e−jωn)−X(ω)∣2dω=0\lim_{N\to\infty} \int_{-\pi}^{\pi}\left|\left(\sum_{k=-N}^N x[k]e^{-j\omega n}\right) - X(\omega)\right|^2d\omega = 0limN→∞​∫−ππ​∣∣​(∑k=−NN​x[k]e−jωn)−X(ω)∣∣​2dω=0
​
Tempered distributions like the Dirac Delta function are other functions which are important for computing the DTFT, and they arise from the theory of generalized functions.
Suppose we want to characterize the signal using its DTFT.
Definition 9
The energy of a deterministic, discrete-time signal x[n]x[n]x[n]
 is given by
​∑n∈Z∣x[n]∣2.\sum_{n\in\mathbb{Z}}|x[n]|^2.∑n∈Z​∣x[n]∣2.
​
The autocorrelation of x[n]x[n]x[n]
, given by a[n]=x[n]∗x∗[−n]a[n] = x[n] * x^*[-n]a[n]=x[n]∗x∗[−n]
, is closely related to the energy of the signal since a[0]=∑n∈Z∣x(n)∣2a[0] = \sum_{n\in\mathbb{Z}}|x(n)|^2a[0]=∑n∈Z​∣x(n)∣2
.
Definition 10
The Energy Spectral Density x[n]x[n]x[n]
 with auto-correlation a[n]a[n]a[n]
 is given by
​A(ejω)=∑n∈Za[n]e−jωnA(e^{j\omega}) = \sum_{n\in\mathbb{Z}}a[n]e^{-j\omega n}A(ejω)=∑n∈Z​a[n]e−jωn
​
We call the DTFT of the autocorrelation the energy spectral density because, by the Inverse DTFT,
​a[0]=12π∫−ππA(ejω)dω.a[0] = \frac{1}{2\pi}\int_{-\pi}^{\pi}A(e^{j\omega})d\omega.a[0]=2π1​∫−ππ​A(ejω)dω.
​
Since summing over each frequency gives us the energy, we can think of A(ejω)A(e^{j\omega})A(ejω)
 as storing the energy density of each spectral component of the signal. We can apply this same idea to wide-sense stationary stochastic processes.
Definition 11
The Power Spectral Density of a Wide-Sense Stationary random process is given by
​SX(ejω)=∑k∈ZRX(k)e−jωk.S_X(e^{j\omega}) = \sum_{k\in\mathbb{Z}}R_X(k)e^{-j\omega k}.SX​(ejω)=∑k∈Z​RX​(k)e−jωk.
​
Note that when considering stochastic signals, the metric changes from energy to power. This is because if XnX_nXn​
 is Wide-Sense Stationary, then
​E[∑n∈Z∣Xn∣2]=∞,\mathbb{E}\left[\sum_{n\in\mathbb{Z}}|X_n|^2\right] = \infty,E[∑n∈Z​∣Xn​∣2]=∞,
​
so energy doesn’t even make sense. To build our notion of power, let AT(ω)A_T(\omega)AT​(ω)
 be a truncated DTFT of the auto-correlation of a wide-sense stationary process, then
​lim⁡T→∞E[AT(ejω)]2T+1=lim⁡T→∞12T+1(∑n=−TTx[n]e−jωn)(∑m=−TTx∗[m]ejωm)=lim⁡T→∞12T+1∑n,m∈[−T,T]E[x[n]x∗[m]]e−jω(n−m)=lim⁡T→∞12T+1∑n,m∈[−T,T]Rx(n−m)e−jω(n−m)=lim⁡T→∞∑k=−2T2TRX(k)e−jωk(1−∣k∣2T+1)=∑k=−∞∞RX(k)e−jωk\begin{aligned} \lim_{T\to\infty} \frac{\mathbb{E}\left[A_T(e^{j\omega})\right] }{2T + 1} &= \lim_{T\to\infty}\frac{1}{2T+1}\left(\sum_{n=-T}^Tx[n]e^{-j\omega n}\right)\left(\sum_{m=-T}^Tx^*[m]e^{j\omega m}\right)\\ &= \lim_{T\to\infty} \frac{1}{2T+1} \sum_{n,m \in [-T,T]}\mathbb{E}\left[x[n]x^*[m]\right] e^{-j\omega(n-m)}\\ &= \lim_{T\to\infty} \frac{1}{2T+1} \sum_{n,m \in [-T,T]}R_x(n-m)e^{-j\omega(n-m)}\\ &= \lim_{T\to\infty} \sum_{k=-2T}^{2T}R_X(k)e^{-j\omega k}\left(1 - \frac{|k|}{2T+1}\right)\\ &= \sum_{k=-\infty}^{\infty}R_X(k)e^{-j\omega k}\end{aligned}T→∞lim​2T+1E[AT​(ejω)]​​=T→∞lim​2T+11​(n=−T∑T​x[n]e−jωn)(m=−T∑T​x∗[m]ejωm)=T→∞lim​2T+11​n,m∈[−T,T]∑​E[x[n]x∗[m]]e−jω(n−m)=T→∞lim​2T+11​n,m∈[−T,T]∑​Rx​(n−m)e−jω(n−m)=T→∞lim​k=−2T∑2T​RX​(k)e−jωk(1−2T+1∣k∣​)=k=−∞∑∞​RX​(k)e−jωk​
​
The DTFT of the auto-correlation function naturally arises out of taking the energy spectral density and normalizing it by time (the truncated sequence is made of 2T+12T+12T+1
 points). In practice, this means to measure the PSD, we need to either use the distribution of the signal to compute RXR_XRX​
, or estimate the PSDPSDPSD
 by averaging multiple realizations of the signal.
The inverse DTFT formula tells us that we can represent a deterministic, discrete-time signal x[n]x[n]x[n]
 as a sum of complex exponentials weighted by X(ejω)dω2π\frac{X(e^{j\omega})d\omega}{2\pi}2πX(ejω)dω​
. This representation has an analog for stochastic signals as well.
Theorem 7 (Cramer-Khinchin)
For a complex-valued WSS stochastic process XnX_nXn​
 with power spectral density SX(ω)S_X(\omega)SX​(ω)
, there exists a unique right-continuous stochastic process F(ω),ω∈(−π,π]F(\omega), \omega\in(-\pi,\pi]F(ω),ω∈(−π,π]
 with square-integrable, orthogonal increments such that
​Xn=∫−ππejωndF(ω)X_n = \int_{-\pi}^{\pi}e^{j\omega n}dF(\omega)Xn​=∫−ππ​ejωndF(ω)
​
where for any interval [ω1,ω2],[ω3,ω4]⊂[−π,π][\omega_1,\omega_2], [\omega_3, \omega_4]\subset [-\pi,\pi][ω1​,ω2​],[ω3​,ω4​]⊂[−π,π]
,
​E[(F(ω2)−F(ω1))(F(ω4)−F(ω3))∗]=f((ω1,ω2]∩(ω3,ω4])\mathbb{E}\left[(F(\omega_2)-F(\omega_1))(F(\omega_4) - F(\omega_3))^*\right] = f((\omega_1,\omega_2] \cap (\omega_3, \omega_4])E[(F(ω2​)−F(ω1​))(F(ω4​)−F(ω3​))∗]=f((ω1​,ω2​]∩(ω3​,ω4​])
​
where fff
 is the structural measure of the stochastic process and has Radon-Nikodym derivative SX(ejω)2π\frac{S_X(e^{j\omega})}{2\pi}2πSX​(ejω)​
.
Besides giving us a decomposition of a WSS random process, Theorem 7 tells a few important facts.
1.​ω1≠ω2  ⟹  ⟨dF(ω1),dF(ω2)⟩=0\omega_1\neq\omega_2 \implies \langle dF(\omega_1), dF(\omega_2) \rangle = 0ω1​=ω2​⟹⟨dF(ω1​),dF(ω2​)⟩=0
 (i.e different frequencies are uncorrelated).
2.​E[∣dF(ω)∣2]=SX(ejω)dω2π\mathbb{E}\left[|dF(\omega)|^2\right] = \frac{S_X(e^{j\omega})d\omega}{2\pi}E[∣dF(ω)∣2]=2πSX​(ejω)dω​
​
Z-Spectrum
Recall that the Z-transform converts a discrete-time signal into a complex representation. It is given by
​X(z)=∑n=−∞∞x[n]z−n.X(z) = \sum_{n=-\infty}^{\infty}x[n]z^{-n}.X(z)=∑n=−∞∞​x[n]z−n.
​
It is a special type of series called a Laurent Series.
Theorem 8
A Laurent Series will converge absolutely on an open annulus
​A={z∣r<∣z∣<R}A = \{z | r < |z| < R \}A={z∣r<∣z∣<R}
​
for some rrr
 and RRR
.
We can compute rrr
 and RRR
 using the signal x[n]x[n]x[n]
.
​r=lim sup⁡n→∞∣x[n]∣1n,1R=lim sup⁡n→∞∣x[−n]∣1n.r = \limsup_{n\to\infty} |x[n]|^{\frac{1}{n}}, \qquad \frac{1}{R} = \limsup_{n\to\infty}|x[-n]|^{\frac{1}{n}}.r=limsupn→∞​∣x[n]∣n1​,R1​=limsupn→∞​∣x[−n]∣n1​.
​
In some cases, it can be useful to only compute the Z-transform of the right side of the signal.
Definition 12
The unilateral Z-transform of a sequence x[n]x[n]x[n]
 is given by
​[X(z)]+=∑n=0∞x[n]z−n\left[X(z)\right]_+ = \sum_{n=0}^\infty x[n]z^{-n}[X(z)]+​=∑n=0∞​x[n]z−n
​
If the Z-transform of the sequence is a rational function, then we can quickly compute what the unilateral Z-transform will be by leveraging its partial fraction decomposition.
Theorem 9
Any arbitrary rational function H(z)H(z)H(z)
 with region of convergence including the unit circle corresponds with the unilateral Z-transform
​[H(z)]+=r0+∑i=1m∑k=1lirik(z+αi)k+∑i=m+1n∑k=1lirikβik\left[H(z)\right]_+ = r_0 + \sum_{i=1}^m\sum_{k=1}^{l_i}\frac{r_{ik}}{(z+\alpha_i)^k} + \sum_{i=m+1}^n\sum_{k=1}^{l_i}\frac{r_{ik}}{\beta_i^k}[H(z)]+​=r0​+∑i=1m​∑k=1li​​(z+αi​)krik​​+∑i=m+1n​∑k=1li​​βik​rik​​
​
where ∣αi∣<1<∣βi∣|\alpha_i| < 1 < |\beta_i|∣αi​∣<1<∣βi​∣
.
Definition 13
For two jointly WSS processes Xn,YnX_n, Y_nXn​,Yn​
, the z-cross spectrum is the Z-Transform of the correlation function RYX(k)=E[YnXn−k∗]R_{YX}(k) = \mathbb{E}\left[Y_nX^*_{n-k}\right] RYX​(k)=E[Yn​Xn−k∗​]
.
​SYX(z)=∑k∈ZRYX(k)z−kS_{YX}(z) = \sum_{k\in\mathbb{Z}}R_{YX}(k)z^{-k}SYX​(z)=∑k∈Z​RYX​(k)z−k
​
Using this definition, we can see that
​SXY(z)=SYX∗(z−∗).S_{XY}(z) = S^*_{YX}(z^{-*}).SXY​(z)=SYX∗​(z−∗).
​
We can also look at the Z-transform of the auto-correlation function of a WSS process XXX
 to obtain SX(z)S_X(z)SX​(z)
.
Definition 14
For a rational function SX(z)S_X(z)SX​(z)
 with finite power (∫−ππSX(ejω)dω<∞)\left(\int_{-\pi}^\pi S_X(e^{j\omega})d\omega < \infty \right)(∫−ππ​SX​(ejω)dω<∞)
 and is strictly positive on the unit circle, the canonical spectral factorization decomposes SX(z)S_X(z)SX​(z)
 into a product of a re>0r_e>0re​>0
 and the transfer function of a minimum phase system L(z)L(z)L(z)
 with L(∞)=1L(\infty) = 1L(∞)=1
​
​SX(z)=L(z)reL∗(z−∗)S_X(z) = L(z)r_eL^*(z^{-*})SX​(z)=L(z)re​L∗(z−∗)
​
Because L(z)L(z)L(z)
 is minimum phase and L(∞)=1L(\infty)=1L(∞)=1
, it must take the form
​L(z)=1+∑i=1∞l[i]z−iL(z) = 1 + \sum_{i=1}^\infty l[i]z^{-i}L(z)=1+∑i=1∞​l[i]z−i
​
since minimum phase systems are causal. Using Definition 14, we can express SX(z)S_X(z)SX​(z)
 as the product of a right-sided and left-sided process.
​SX(z)=(reL(z))(reL∗(z−∗))=SX+(z)SX−(z)S_X(z) = (\sqrt{r_e}L(z))(\sqrt{r_e}L^*(z^{-*})) = S_X^+(z)S_X^-(z)SX​(z)=(re​​L(z))(re​​L∗(z−∗))=SX+​(z)SX−​(z)
​
Note that SX−(ejω)=(SX+(ejω))∗S_X^-(e^{j\omega}) = \left(S_X^+(e^{j\omega})\right)^*SX−​(ejω)=(SX+​(ejω))∗
. Using the assumptions built into Definition 14, we can find a general form for L(z)L(z)L(z)
 since we know SY(z)S_Y(z)SY​(z)
 takes the following form
​SY(z)=re∏i=1m(z−αi)(z−1−αi∗)∏i=1n(z−βi)(z−1−βi∗)∣αi∣<1,∣βi∣<1,re>0.S_Y(z) = r_e \frac{\prod_{i=1}^m(z-\alpha_i)(z^{-1}-\alpha_i^*)}{\prod_{i=1}^n(z-\beta_i)(z^{-1}-\beta_i^*)}\quad |\alpha_i| < 1, |\beta_i| < 1, r_e > 0.SY​(z)=re​∏i=1n​(z−βi​)(z−1−βi∗​)∏i=1m​(z−αi​)(z−1−αi∗​)​∣αi​∣<1,∣βi​∣<1,re​>0.
​
If we let the z−αiz - \alpha_iz−αi​
 and z−βiz-\beta_iz−βi​
 terms be part of L(z)L(z)L(z)
, then
​L(z)=zn−m∏i=1m(z−αi)∏i=1n(z−βi).L(z) = z^{n-m}\frac{\prod_{i=1}^m(z-\alpha_i)}{\prod_{i=1}^n(z-\beta_i)}.L(z)=zn−m∏i=1n​(z−βi​)∏i=1m​(z−αi​)​.
​
Markov Processes
Definition 15
We say that random variables X,Y,ZX, Y, ZX,Y,Z
 form a Markov Triplet 
 if and only if XXX
 and ZZZ
 are conditionally independent on YYY
​
Mathematically, Markov triplets satisfy three properties.
1.​p(x,z∣y)=p(x∣y)p(z∣y)p(x, z | y) = p(x|y)p(z|y)p(x,z∣y)=p(x∣y)p(z∣y)
​
2.​p(z∣x,y)=p(z∣y)p(z|x, y) = p(z|y)p(z∣x,y)=p(z∣y)
​
3.​p(x∣y,z)=p(x∣y)p(x|y, z) = p(x|y)p(x∣y,z)=p(x∣y)
​
Because of these rules, the joint distribution can be written as p(x,y,z)=p(x)p(y∣x)p(z∣y)p(x, y, z) = p(x)p(y|x)p(z|y)p(x,y,z)=p(x)p(y∣x)p(z∣y)
.
Theorem 10
Random variables X,Y,ZX,Y,ZX,Y,Z
 form a Markov triplet if and only if there exist ϕ1,ϕ2\phi_1, \phi_2ϕ1​,ϕ2​
 such that p(x,y,z)=ϕ1(x,y)ϕ2(y,z)p(x, y, z) = \phi_1(x, y)\phi_2(y, z)p(x,y,z)=ϕ1​(x,y)ϕ2​(y,z)
.
To simplify notation, we can define Xmn=(Xm,Xm+1,⋯ ,Xn)X_m^n = \left(X_m,X_{m+1},\cdots, X_n\right)Xmn​=(Xm​,Xm+1​,⋯,Xn​)
 and Xn=X1nX^n=X_1^nXn=X1n​
.
Definition 16
A Markov Process is a Discrete Time Random Process {Xn}n≥1\{X_n\}_{n\geq1}{Xn​}n≥1​
 where 
 for all n≥2n\geq 2n≥2
​
Because of the conditional independence property, we can write the joint distribution of all states in the Markov process as
​p(xn)=∏t=1np(xt∣xt−1)=∏t=1np(xt∣xt−1).p(x^n) = \prod_{t=1}^n p(x_t|x^{t-1}) = \prod_{t=1}^np(x_t|x_{t-1}).p(xn)=∏t=1n​p(xt​∣xt−1)=∏t=1n​p(xt​∣xt−1​).
​
The requirement for 
 to satisfy p(x,y,z)=p(x)p(y∣x)p(z∣y)p(x, y, z) = p(x)p(y|x)p(z|y)p(x,y,z)=p(x)p(y∣x)p(z∣y)
 is a very strict requirement. If we wanted to create a “wider” requirement of Markovity, then we could settle for X^(Y)=X^(Y,Z)\hat{X}(Y) = \hat{X}(Y, Z)X^(Y)=X^(Y,Z)
 where X^\hat{X}X^
 is the best linear estimator of XXX
 since this property is satisfied by all Markov triplets, but does not imply a Markov Triplet.
Definition 17
Random variables X,Y,ZX, Y, ZX,Y,Z
 form a Wide Sense Markov Triplet 
 if and only if the best linear estimator of X given Y is identical to the best linear estimator of X given Y and Z.
​X^(Y)=X^(Y,Z)\hat{X}(Y) = \hat{X}(Y, Z)X^(Y)=X^(Y,Z)
​
Definition 18
A stochastic process {Yi}i=0n\{Y_i\}_{i=0}^n{Yi​}i=0n​
 is a Wide-Sense Markov Process if and only if for any 1≤i≤n−11 \leq i \leq n - 11≤i≤n−1
, 
forms a Wide-Sense Markov Triplet.
All Wide-Sense Markov models have a very succint representation.
Theorem 11
A process X\boldsymbol{X}X
 is Wide-Sense Markov if and only if Xi+1=FiXi+GiUi\boldsymbol{X}_{i+1} = F_i \boldsymbol{X}_i + G_i \boldsymbol{U}_iXi+1​=Fi​Xi​+Gi​Ui​
 and
​⟨[UiX0],[UjX0]⟩=[Qiδ[i−j]00Π0]\langle \begin{bmatrix} U_i \\ \boldsymbol{X}_0 \end{bmatrix}, \begin{bmatrix} U_j \\ \boldsymbol{X}_0 \end{bmatrix} \rangle = \begin{bmatrix} Q_i \delta[i-j] & 0\\ 0 & \Pi_0 \end{bmatrix}⟨[Ui​X0​​],[Uj​X0​​]⟩=[Qi​δ[i−j]0​0Π0​​]
​
Hidden Markov Processes
Definition 19
If {Xn}n≥1\{X_n\}_{n\geq1}{Xn​}n≥1​
 is a Markov Process, then {Yn}n≥1\{Y_n\}_{n\geq1}{Yn​}n≥1​
 is a Hidden Markov Process if we can factorize the conditional probability density
​p(yn,xn)=∏i=1np(yi∣xi)p(y^n, x^n) = \prod_{i=1}^np(y_i|x_i)p(yn,xn)=∏i=1n​p(yi​∣xi​)
​
We can think of YYY
 as a noisy observation of an underlying Markov Process. The joint distribution of {Xn}n≥1\{X_n\}_{n\geq1}{Xn​}n≥1​
 and {Yn}n≥1\{Y_n\}_{n\geq1}{Yn​}n≥1​
 can be written as
​p(xn,yn)=p(xn)p(yn∣xn)=∏t=1np(xt∣xt−1)∏i=1np(yi∣xi).p(x^n, y^n) = p(x^n)p(y^n|x^n) = \prod_{t=1}^np(x_t|x_{t-1})\prod_{i=1}^np(y_i|x_i).p(xn,yn)=p(xn)p(yn∣xn)=∏t=1n​p(xt​∣xt−1​)∏i=1n​p(yi​∣xi​).
​
Hidden Markov Models can be represented by undirected graphical models. To create an undirected graphical model,
1.Create a node for each random variable.
2.Draw an edge between two nodes if a factor of the joint distribution contains both nodes.
Undirected graphical models of Hidden Markov Processes are useful because they let us derive additional Markov dependepencies between groups of variables.
Theorem 12
For 3 disjoint sets S1,S2,S3S_1, S_2, S_3S1​,S2​,S3​
 of notes in a graphical model, if any path from S1S_1S1​
 to S3S_3S3​
 passes through a node in S2S_2S2​
, then 
.
State-Space Models
Suppose we have a discrete-time random process which evolves in a recursive fashion, meaning the current state depends in some way on the previous state. We can express this recursion with a set of equations.
Definition 20
The standard state space model describes random processes which describe the evolution of state vectors Xi\boldsymbol{X}_iXi​
 and observation vectors Yi\boldsymbol{Y}_iYi​
 according to the equations
​{Xi+1=FiXi+GiUiYi=HiXi+Vi\begin{cases} \boldsymbol{X}_{i+1} = F_i \boldsymbol{X}_i + G_i \boldsymbol{U}_i\\ \boldsymbol{Y}_{i} = H_i\boldsymbol{X}_i + \boldsymbol{V}_i \end{cases}{Xi+1​=Fi​Xi​+Gi​Ui​Yi​=Hi​Xi​+Vi​​
​
with initial condition
​⟨[X0UiVi],[X0UjVj]⟩=[Π0000Qiδ[i−j]Siδ[i−j]0Si∗δ[i−j]Riδ[i−j]]\langle \begin{bmatrix}\boldsymbol{X}_0 \\ \boldsymbol{U}_i \\ \boldsymbol{V}_i\end{bmatrix}, \begin{bmatrix}\boldsymbol{X}_0 \\ \boldsymbol{U}_j \\ \boldsymbol{V}_j\end{bmatrix} \rangle = \begin{bmatrix} \Pi_0 & 0 & 0\\ 0 & Q_i\delta[i-j] & S_i\delta[i-j]\\ 0 & S_i^*\delta[i-j] & R_i\delta[i-j] \end{bmatrix}⟨⎣⎡​X0​Ui​Vi​​⎦⎤​,⎣⎡​X0​Uj​Vj​​⎦⎤​⟩=⎣⎡​Π0​00​0Qi​δ[i−j]Si∗​δ[i−j]​0Si​δ[i−j]Ri​δ[i−j]​⎦⎤​
​
From Theorem 11, we can easily see that state space models are Wide-Sense Markov. Note that UiU_iUi​
 and ViV_iVi​
 are white noise, and that the dynamics of the system can change at every time step. From these equations, we can derive six different properties. Let Πi=⟨Xi,Xi⟩\Pi_i = \langle \boldsymbol{X}_i, \boldsymbol{X}_i \rangle Πi​=⟨Xi​,Xi​⟩
and Φi,j=∏k=ji−1Fk\Phi_{i,j} = \prod_{k=j}^{i-1}F_kΦi,j​=∏k=ji−1​Fk​
 and Φi,u=I\Phi_{i,u} = IΦi,u​=I
.
1.​∀i≥j, ⟨Ui,Xj⟩=0, ⟨Vi,Xj⟩=0\forall i \geq j,\ \langle \boldsymbol{U}_i, \boldsymbol{X}_j \rangle = 0,\ \langle \boldsymbol{V}_i, \boldsymbol{X}_j \rangle = 0∀i≥j, ⟨Ui​,Xj​⟩=0, ⟨Vi​,Xj​⟩=0
​
2.​∀i>j, ⟨Ui,Yj⟩=0, ⟨Vi,Yj⟩=0\forall i > j,\ \langle \boldsymbol{U}_i, \boldsymbol{Y}_j \rangle = 0,\ \langle \boldsymbol{V}_i, \boldsymbol{Y}_j \rangle = 0∀i>j, ⟨Ui​,Yj​⟩=0, ⟨Vi​,Yj​⟩=0
​
3.​∀i, ⟨Ui,Yi⟩=Si, ⟨Vi,Yi⟩=Ri\forall i,\ \langle \boldsymbol{U}_i, \boldsymbol{Y}_i \rangle = S_i,\ \langle \boldsymbol{V}_i, \boldsymbol{Y}_i \rangle = R_i∀i, ⟨Ui​,Yi​⟩=Si​, ⟨Vi​,Yi​⟩=Ri​
​
4.​Πi+1=FiΠiFi∗+GiQiGi∗\Pi_{i+1} = F_i\Pi_iF_i^* + G_iQ_iG_i^*Πi+1​=Fi​Πi​Fi∗​+Gi​Qi​Gi∗​
​
5.​
$\langle \boldsymbol{X}_i, \boldsymbol{X}_j \rangle  = \begin{cases}                 \Phi_{i,j}\Pi_j & i \geq j \\                 \Pi_i \Phi_{j,i}^* & i \leq j             \end{cases}$
1.​
$\langle \boldsymbol{Y}_i, \boldsymbol{Y}_j \rangle  = \begin{cases}                  H_i \Phi_{i,j+1}N_j & i > j\\                  R_i + H_i\Pi_iH_i^* & i=j \\                  N_i^*\Phi^*_{j,i+1}H_j^* & i < j             \end{cases} \text{ where } N_i=F_i\Pi_iH_i^*+G_iS_i$
Linear Estimation
Filtering
Last modified 7mo ago
Copy link
Outline
Wide-Sense Stationary Random Processes
Spectral Density
Z-Spectrum
Markov Processes
Hidden Markov Processes
State-Space Models