RESEARC H Open Access On general filtering problem of stationary processes with fixed transformation Long suo Li Correspondence: lilongsuo6982@126.com Department of Mathematics, Harbin Institute of Technology, Harbin 150001, P.R.China Abstract A fixed transformation are given for one-dimensional stationary processes in this paper. Based on this, we propose a general filtering problem of stationary proce sses with fixed transformation. Finally, on a stationary processes with no any additional conditions, we get the spectral characteristics of P H η ( t ) ξ in the space L 2 (F X (dl)), and then we calculate the value of the best predict quantity Q of the general filtering problem. Keywords: stationary proces ses, fixed transformation, fillering 1. Introduction The Prediction theory is an important part of stationary processes, also linear filter problems is an important part of Prediction theory. The linear filtering problem of multidimensional stationary sequence and processes for a linear system are firsted stu- died by Rosanov in [1], and then a series of general filter problem of stationary process for a linear system are studied in [2-9]. Theoretica lly, this problem is a extend of the classic prediction problem. But it has high practical value, it also widely applied in communication, exploration, space technology and automatic control, etc. 2. Propose the problem Let X(t),tÎ R be (simple) wide stationary process. Let H X = L{X(t), t ∈ R} H X ( t ) = L{X ( s ) , s ≤ t, s ∈ R } Suppose the complex-value function b (t) statisfing the following conditions 1) b ( t ) ∈ L [0, +∞ ) ∩ L 2 [0, +∞ ) (2 À 1) 2) b ( t ) for t < 0 (2 À 2) Let B(λ)= ∞ 0 b(t)e −iλt dt (2 À 3) Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 © 2011 L i; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestrict ed use, distribution, and reproduction in any medium, provided the original work is properly cited. then B(l) is boundary values analytic function B(z) in the low half plane We can obtain B ( λ ) =0, a.e. Le b Let E = {λ : B ( λ ) =0}, E = ( −∞, ∞ ) − E (2 À 4) then L ( E ) = 0 Let η(t)= ∞ 0 b(s) · X(t − s ) ds = ∞ 0 b(s)U −s X(t)d s (2 À 5) where U is the shift oprator of X(t) in the space H X . Then, for each ξ Î H X 1) Find out the value of Q, Q =inf ξ∈H X ( t ) ∞ 0 b (s)U −s ξ ds − ξ 2 (2 À 6) 2) Then, we will prove that Q = ||P H η (t) ξ − ξ || 2 and solve the spectral characteristics of P H η (t ) in the space L 2 (F X (dl)). 3. Main result Let the random spectral measure of X(t)isF X (dl), and the spectral measure is F X (dl), the broad spectral measure which also named the spectral measure of absolutely continuous part of F is f X (l). The Lebesgue decomposition of F X (dl)is F X ( dλ ) = f X ( λ ) dλ + δ ( dλ ) (3 À 1) where the Lebesgue measure of δ(dl) is singular, namely δ(l)=c Δ (l)F X (dl) ⊂ ( −∞, ∞ ) , L ( ) =0, = ( −∞, ∞ ) − Let A t = h : h = ∞ 0 b(s)U −s yds, y ∈ H X (t ) (3 À 2) Obviously, At is linear set. Lemma 1. Let X(t),tÎ R is stationary processes, F (dl) and Z(dl) are spectral mea- sure and random spectral measure respectively. ∀f(l), (l) Î L 2 (F), and |(l)| ≤ M, M >0, where M is real number, we have Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 2 of 10 ∞ −∞ f (λ)ϕ(λ)Z(dλ) ≤ M ∞ ∞ f (λ)Z(dλ) Proof. According to the nature of the random integral, we have ∞ −∞ f (λ)ϕ(λ)Z(dλ) = ∞ −∞ |f (λ)ϕ(λ)| 2 F( dλ) 1 2 = ∞ −∞ |f (λ)| 2 ·|ϕ(λ)| 2 F( dλ) 1 2 ≤ M ∞ −∞ |f (λ)| 2 · F(dλ) 1 2 = M ∞ −∞ f (λ)Z(dλ) Lemma 2. L {A t } = H η (t ) (3 À 3) where L ( A t ) is the linear closed manifold of A t . Proof. ∀h (τ 0 ), τ 0 ≤ t, Let y = X(τ 0 ), we have y Î H X (t), and η(τ 0 )= ∞ 0 b(s)U −s yds = ∞ 0 b(s)U −s X(τ 0 )d s namely, h (τ 0 ) Î A t , then H η (t ) ⊂ L (A t ) On the other hand, ∀h Î A t , we have h = ∞ 0 b(s)U −s yd s where y Î H X (t). Let z l = m l k =1 a l k X(t l k ), t l k ≤ t, k =1,2,··· , m l , a l k , is complex number. Let | |y − z l || → 0 ( l →∞ ) while ∞ 0 b(s)U −s z l ds = m l k =1 a l k ∞ 0 b(s)U −s X(t l k )ds ∈ H η (t ), l =1,2, ·· · Let, the spectral characteristics of y and z t are ψ y (l)and ψ z l (λ ) in the s pace L 2 (F X (dl)) respectively. According to the equation (2-3) Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 3 of 10 | B(λ)| = ∞ 0 b(s)e −iλs ds ≤ ∞ 0 |b(s)|ds ∧ = M where M>0 is constant. According to the lemma 1, we get h − ∞ 0 b(s)U −s z l ds = ∞ 0 b(s)U −s (y − z l )ds = ∞ 0 b(s) ∞ −∞ e −isλ (ψ y (λ) − ψ z l (λ)) X (dλ)ds = ∞ −∞ (ψ y (λ) − ψ z l (λ)) ∞ 0 b(s)e −isλ ds X (dλ) = ∞ −∞ (ψ y (λ) − ψ z l (λ))B(λ) X (dλ) ≤ M ∞ −∞ (ψ y (λ) − ψ z l (λ)) X (dλ) = M ∞ −∞ |ψ y (λ) − ψ z l (λ)| 2 F( dλ) 1 2 = M||y − z l || → 0 ( l →∞ ) so, h Î H h (t), namely L{A t }⊂H η (t ) Then, it shows the equation (3-3) is correct. According to the lemma 2, we have Q =inf ξ∈H X (t) ∞ 0 b(s)U −s ξds − ξ 2 =inf h∈A t ||h − ξ|| 2 =inf h∈L{A t } ||h − ξ|| 2 =inf h∈H η (t) ||h − ξ|| 2 = ||P H η (t) ξ − ξ || 2 (3 À 4) According to the equation (2-5) and (2-1), we get η(t)= ∞ 0 b(s)X(t − s)ds = ∞ 0 b(s) ∞ −∞ e i(t−s)λ X (dλ)ds = ∞ −∞ e itλ ∞ 0 b(s)e −isλ ds X (dλ)= ∞ −∞ e itλ B(λ) X (dλ ) on the other hand η(t)= ∞ − ∞ e itλ η (dλ ) According to the stochastic process spectral theorem and the Relevant function spec- tral theorem, we have Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 4 of 10 η (dλ)=B(λ) X (dλ ) (3 À 5) F η (dλ)= |B(λ)| 2 F X (dλ ) (3 À 6) f η (dλ)= |B(λ)| 2 f X (λ ) (3 À 7) where F h (dl),F h (dl),f h (l) representative the random spectral measure, spectral measure and broad spectral measure of h(t). Lemma 3. Let ξ Î H X , ξ = ξ + ξ , where ξ ∈ H η , ξ⊥H η , then Q = || ξ|| 2 + ||P H η (t) ξ − ξ|| 2 (3 À 8) If, the wold decomposition of h (t)is η ( t ) = η r ( t ) + η s ( t ) and ξ = ξ r + ξ s where h r is regular process, h s (t) is singular process, and ξ r ⊥S η · ξ s ∈ S η , S η = t H η (t ) , then Q = || ξ || 2 + ||P H η r(t) ξ r − ξ r || 2 (3 À 9) Proof. According to ξ = ξ + ξ , ξ ∈ H η , ξ⊥H η ,So P H η (t) ξ = P H η (t) ξ + P H η (t) ξ = P H η (t) ξ According to the equation (3-4), Q = ||P H η (t) ξ − ξ || 2 = ||P H η (t) ξ − ( ξ + ξ)|| 2 = ||(P H η (t) ξ − ξ) − ξ|| 2 also, according to ξ⊥(P H η (t) ξ − ξ ) ,So Q = || ξ|| 2 + ||P H η (t) ξ − ξ|| 2 namely, the equation (3-8) is correct. When h (t) have the wold decomposition, notice ξ S = P H η ξ, H η (t )=H η r (t ) ⊕ S η we get P H η (t) ξ s = P H η (t) (P S η ξ )=P S η ξ = ξ s (3 À 10) P H η (t) ξ r = P H η r (t) ξ r + P S η ξ r = P H η r (t) ξ r (3 À 11) So ||P H η (t) ξ − ξ|| 2 = ||(P H η (t) ξ r + P H η (t) ξ s − ( ξ r + ξ s )|| 2 = ||P H η r (t) ξ r − ξ r || 2 Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 5 of 10 Then, according to the equation (3-8), we get that the equation (3-9) is correct. Lemma 4.Letξ Î H X , ξ = ξ + ξ ,where ξ ∈ H η , ξ⊥H η , ψ(l) is spectral characteristics of ξ in the space of L 2 (F X (dl)), ψ ( λ ) is spectral characteristics of ξ in the space of L 2 (F h (dl)), then 1) when λ ∈ E , ψ ( λ ) = ψ ( λ ) B ( λ ) , a.e. F X ( dλ ) (3 À 12) 2) | | ξ|| 2 = E |ψ(λ)| 2 F X (dλ ) (3 À 13) Proof. 1) According to the given conditions, we have ξ = ∞ −∞ ψ(λ) X (dλ) ξ = ∞ −∞ ψ(λ) η (dλ)= ∞ −∞ ψ(λ)B(λ) X (dλ ) η(t)= ∞ − ∞ e itλ η (dλ)= ∞ − ∞ e itλ B(λ) X (dλ) So (ξ, η(−t)) = ∞ − ∞ e itλ ψ(λ)B(λ)F X (dλ ) on the other hand (ξ, η(−t)) = ( ξ + ξ, η(−t)) = ( ξ, η(−t) ) = ∞ − ∞ e itλ ψ(λ)|B(λ)| 2 F X (dλ) According to the Fourier transformation, we have ψ ( λ ) B ( λ ) = ψ ( λ ) |B ( λ ) | 2 ,a.e.F X ( dλ ) So, when λ ∈ E , we have ψ ( λ ) = ψ ( λ ) B ( λ ) ,a.e.F X ( dλ ) 2) According to ξ = ξ + ξ , and ξ ⊥ ξ , Thus || ξ || 2 = || ξ + ξ || 2 = || ξ || 2 + || ξ || 2 || ξ|| 2 = ||ξ|| 2 −|| ξ || 2 = ∞ −∞ |ψ(λ)| 2 F X (dλ) − ∞ −∞ | ψ(λ)| 2 F η (dλ) = ∞ −∞ |ψ(λ)| 2 F X (dλ) − ∞ −∞ | ψ(λ)|·|B(λ)| 2 F X (dλ) = ∞ −∞ χ E (λ)|ψ(λ)| 2 F X (dλ)+ ∞ −∞ χ E (λ)|ψ(λ)| 2 F X (dλ) − ∞ −∞ χ E (λ)| ψ(λ)| 2 ·|B(λ)| 2 F X (dλ ) = ∞ − ∞ χ E (λ)|ψ(λ)| 2 F X (dλ)= E |ψ(λ)| 2 F X (dλ) Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 6 of 10 namely, the equation (3-13) is correct. Theorem.Letξ Î H X , ξ = ξ + ξ ,where ξ ∈ H η , ξ⊥H η , ψ(l) repr esentative the spec- tral characteristics of ξ in the space of L 2 ( F X (dl)), ψ ( λ ) representative the spectral characteristics of ξ in the space of L 2 (F h (dl)). Then 1) When log f X (λ) 1+ λ 2 /∈ L 1 (−∞, ∞ ) Q = E |ψ(λ)| 2 F X (dλ ) (3 À 14) now the spectral characteristics of P H η ( t ) ξ in the space of L 2 (F X (dl)) is ψ(λ)= ⎧ ⎨ ⎩ 0, λ ∈ E, a.e. F X (dλ ) ψ(λ), λ ∈ E. (3 À 15) 2) When log f X (λ) 1+λ 2 ∈ L 1 (−∞, ∞ ) Q = E |ψ(λ)| 2 F X (dλ)+ ∞ t |ϕ(−s)| 2 d s (3 À 16) now the spectral characteristics of P H η ( t ) ξ in the space of L 2 (F X (dl)) is ψ(λ)= ⎧ ⎪ ⎨ ⎪ ⎩ 0, λ ∈ E, ψ(λ), λ ∈ E , B(λ) ∞ −t e −isλ ϕ(s)ds ( λ ) , λ ∈ E (3 À 17) where b(t),B(l), h(t),Eand Δ is decided by the equation (2-1), (2-2), (2-3), (2-5), (2- 4) and (3-1) respectively. Where Γ(l) is the maximal factor boundary values of the spectral density f h (l) , (l) is the Fourier transformation of ψ ( λ ) ( λ ) ,where ψ ( λ ) is determined by equation (3-12) and ψ ( λ ) = ψ ( λ ) /B ( λ ) , a.e. L. Proof. 1) When log f X (λ) 1+λ 2 /∈ L 1 (−∞, ∞ ) . According to f η (λ)= |B(λ)| 2 f X (λ ) (3 À 18) log |B ( λ ) |∈L 1 ( −∞, ∞ ) (3 À 19) we know that log f η (λ) 1+λ 2 /∈ L 1 (−∞, ∞ ) Thus, h(t) is singular process. So H η = S η P H η (t) ξ = P H η ξ = P S η ξ = ξ According to (3-8) Q = || ξ|| 2 = E |ψ(λ)| 2 F X (dλ) . Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 7 of 10 On the other hand P H η (t) ξ = P H η ( ξ + ξ )=P H η ξ = P S η ξ = ξ The spectral characteristics of P H η ( t ) ξ in the space L 2 (F X (dl)) is ψ ( λ ) = ψ ( λ ) B ( λ ) , a.e. F X ( dλ ) According to the equation (3-12), we get ψ(λ)= ⎧ ⎨ ⎩ 0, λ ∈ E, a.e. F X (dλ ) ψ(λ), λ ∈ E, 2) When log f X (λ) 1+ λ 2 ∈ L 1 (−∞, ∞ ) , according to (3-18) and (3-19), we get log f η (λ) 1+ λ 2 ∈ L 1 (−∞, ∞ ) . It shows that h(t) is non-singular process, so h(t) has regular singular decomposition, and it consistent with Lebesgue-Gramer decompo sition. Let the decomposition equa- tion is η ( t ) = η r ( t ) + η s ( t ) where h r (t) is regular process, h s (t) is singular process η r (t )= ∞ −∞ e itλ η r (dλ)= ∞ −∞ e itλ χ (λ) η (dλ ) η s (t )= ∞ − ∞ e itλ η s (dλ)= ∞ − ∞ e itλ χ (λ) η (dλ) Let V (ds) is basic cross stochastic measure, namely V ( 1 )= ∞ − ∞ e iλt 2 − e iλt 1 iλ (dλ ) where Δ 1 =(t 1 ,t 2 ], stochastic measure (dλ)= 1 (λ) η r (dλ ) , Thus η r (t )= t − ∞ C(t − s)V(ds ) when C(s)= 1 2 π ∞ −∞ e isλ (λ)d λ , and Γ(l) satisfate the follow conditions f (λ)= 1 2 π |(λ)| 2 also Γ(l) is the boundary values of the lower half plane maximum analytic functions Γ(z), notice ξ r = ∞ −∞ ψ(λ)χ (λ) η (dλ)= ∞ −∞ ψ(λ) η r (dλ ) = ∞ −∞ ψ(λ)(λ)(dλ) ξ s = ∞ − ∞ ψ(λ)χ (λ) η (dλ)= ∞ − ∞ ψ(λ) η s (dλ) Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 8 of 10 Let (s) is the Fourier transforation of ψ ( λ ) ( λ ) , namely ϕ(s)= 1 2π ∞ − ∞ e isλ ψ(λ)(λ)d λ According to the Fourier transformation of random measure ξ r = ∞ −∞ ψ(λ)(λ)(dλ)= ∞ −∞ ∞ −∞ e −iλs ϕ(s)ds(dλ ) = ∞ − ∞ ∞ − ∞ e iλs ϕ(−s)ds(dλ)= ∞ − ∞ ϕ(−s)V(ds) Thus P H η r (t) ˆ ξ r = t −∞ ϕ(−s)V(ds) | |P H η r (t) ˆ ξ r − ˆ ξ r || 2 = ∞ t ϕ(−s)V(ds) 2 = ∞ t |ϕ(−s)| 2 d s According to the equation (3-9), we get Q = E |ψ(λ)| 2 F X (dλ)+ ∞ t | ϕ(−s)| 2 d s According to the Lemma 3 P H η (t) ξ = P H η (t) ξ = P H η (t) ξ r + P H η (t) ξ s = P H η r (t) ξ r + ξ s notice P H η r (t) ξ r = t −∞ ϕ(−s)V(ds)= ∞ −∞ t −∞ e iλs ϕ(−s)ds (dλ) = ∞ −∞ ∞ −t e −iλs ϕ(s)ds 1 (λ) η r (dλ) = ∞ −∞ ∞ −t e −iλs ϕ(s)ds 1 (λ) χ (λ)B(λ) X (dλ) ξ s = ∞ − ∞ ψ(λ)χ (λ) η (dλ)= ∞ − ∞ ψ(λ)χ (λ)B(λ) X (dλ ) Thus P H η (t) ξ = ∞ −∞ ∞ −t e −iλs ϕ(s)ds B(λ) ( λ ) χ (λ)+ ψ(λ)B(λ)χ (λ) X (dλ ) So, the spectral characteristics of P H η ( t ) ξ in the space of L 2 (F X (dl)) is ψ(λ)= ⎧ ⎪ ⎨ ⎪ ⎩ 0, λ ∈ E ψ(λ), λ ∈ E B(λ) ∞ −t e −iλs ϕ(s)ds ( λ ) , λ ∈ E 4. Conclusions A fixed transform is given which based on one-dimensional a stationary processes in this paper. Also, we propose a general filtering problem. Then, in the space of L 2 (F X (dl)), we get the spectral characteristics of P H η ( t ) ξ with no any additional conditions. Finally, we calculate the value of the best predict quantity of the general filtering problem. Li Journal of Inequalities and Applications 2011, 2011:68 http://www.journalofinequalitiesandapplications.com/content/2011/1/68 Page 9 of 10 Acknowledgements This work was supported by the Natural Science Foundation of China (Grant no. 10771047). Authors’ contributions The studies and manuscript of this paper was written by Longsuo Li independently. Competing interests The author declares that they have no competing interests. Received: 5 May 2011 Accepted: 23 September 2011 Published: 23 September 2011 References 1. 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Journal of Henan Normal University (Natural Science)., 3 (1983) 7. Zhang, XY, Xie, ZJ: On the extrapolation of multivariate stationary time series for a linear system. Journal of Henan Normal University(Natural Science)., 4 (1983) 8. Xie, ZJ, Cheng, QS: On the extrapolation of stationary time series for a linear system. Fifth European Meating on Cybesneties and Systems Research(edited by austrian society for cybernetie studies). (1980) 9. Zhang, XY: A few problems in the extrapolation of multivariate stationary time series for a linear system. Journal of Henan Normal University(Natural Science)., 4 (1984) doi:10.1186/1029-242X-2011-68 Cite this article as: Li: On general filtering problem of stationary processes with fixed transformation. Journal of Inequalities and Applications 2011 2011:68. 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Keywords: stationary proces ses, fixed transformation, fillering 1. Introduction The Prediction theory is an important part of stationary processes,