Convergence of Particle Filtering Method for Nonlinear Estimation of Vortex Dynamics

Transcription

1 Convergence of Particle Filtering Method for Nonlinear Estimation of Vortex Dynamics Meng Xu Department of Mathematics University of Wyoming February 20, 2010

2 Outline 1 Nonlinear Filtering Stochastic Vortex Model Particle Filter 2 Solvability of Zakai Equation Convergence of Solutions to Zakai Equation

3 Outline 1 Nonlinear Filtering Stochastic Vortex Model Particle Filter 2 Solvability of Zakai Equation Convergence of Solutions to Zakai Equation

4 Nonlinear Filtering Stochastic Vortex Model Particle Filter Introduction to Nonlinear Filtering We begin with a complete probability space (Ω, F, P) on which our stochastic process will be defined. Consider the stochastic differential equation for the signal process X t : dx t = f (X t )dt + σ(x t )dw t (1) Observation process is defined as: dy t = h(x t )dt + db t (2) where W t and B t are uncorrelated noises.

5 Nonlinear Filtering Stochastic Vortex Model Particle Filter Introduction to Nonlinear Filtering We begin with a complete probability space (Ω, F, P) on which our stochastic process will be defined. Consider the stochastic differential equation for the signal process X t : dx t = f (X t )dt + σ(x t )dw t (1) Observation process is defined as: dy t = h(x t )dt + db t (2) where W t and B t are uncorrelated noises.

6 Nonlinear Filtering Stochastic Vortex Model Particle Filter Introduction to Nonlinear Filtering The nonlinear filtering problem is to calculate the following conditional expectation π t (ϕ) = E[ϕ(X t ) Y t ] (3) which is the least square estimate for ϕ(x t ) given Y t and satisfies a nonlinear stochastic differential equation, called the Fujisaki-Kallianpur-Kunita [FKK] equation. It is shown that π t (x) can be represented as π t (x) = ρ t (x)/ ρ t (x)dx (4) R d with ρ t (x), unnormalized filtering density, satisfies a linear stochastic differential equation, called the Zakai equation.

7 Biot-Savart Law Nonlinear Filtering Stochastic Vortex Model Particle Filter By the Green function technique, one can express the relation between u and ω as Biot-Savart Law: u = u + K ω. (5) where K is the rotational part of the Green function, K(x) = (2π x 2 ) 1 ( x 2, x 1 ). (6) Numerically, it is difficult to handle because of its singularity.

8 Stochastic Vortex Model Nonlinear Filtering Stochastic Vortex Model Particle Filter Denote X i (t) as the position for the i-th point vortice with initial data ξ i, then t t X i (t) = ξ i + u ɛ,s (X i (s))ds+ σ(x i (s))dw s, for i = 1,, N 0 0 (7) with N u ɛ,t (x) = α j K ɛ (x x j (t)), x R 2. (8) j=1 Equation (7) will be the signal process in the nonlinear filtering problem.

9 Idea of Particle Filter Nonlinear Filtering Stochastic Vortex Model Particle Filter Numerical method for solving Zakai equation. The Monte-Carlo method with correction step. Remove the unlikely particles and multiply those situated in the right areas. Particle filter is also called the sequential Monte-Carlo method.

10 Idea of Particle Filter Nonlinear Filtering Stochastic Vortex Model Particle Filter Numerical method for solving Zakai equation. The Monte-Carlo method with correction step. Remove the unlikely particles and multiply those situated in the right areas. Particle filter is also called the sequential Monte-Carlo method.

11 Idea of Particle Filter Nonlinear Filtering Stochastic Vortex Model Particle Filter Numerical method for solving Zakai equation. The Monte-Carlo method with correction step. Remove the unlikely particles and multiply those situated in the right areas. Particle filter is also called the sequential Monte-Carlo method.

12 Idea of Particle Filter Nonlinear Filtering Stochastic Vortex Model Particle Filter Numerical method for solving Zakai equation. The Monte-Carlo method with correction step. Remove the unlikely particles and multiply those situated in the right areas. Particle filter is also called the sequential Monte-Carlo method.

13 Uniqueness Result Solvability of Zakai Equation Convergence of Solutions to Zakai Equation Definition An Y t -adapted stochastic process µ t taking value in M(R 2N ) is said to be a measure valued solution to the Zakai equation corresponding to the initial condition µ 0 (dx) = P(x 0 dx Y 0 ), if µ, 1 L 2 ([0, T ] Ω; dt dp), for every t T and T <, µ t, 1 L 2 (Ω, dp) and for any ψ C 2 b (R2N ) the following equality holds P-almost surely. µ t, ψ = µ 0, ψ + t µ s, Lψ ds 0 t + 0 µ s, Mψ dy s, t [0, T ]. (9)

14 Uniqueness Result Solvability of Zakai Equation Convergence of Solutions to Zakai Equation Theorem Assume a(x) = 1 2 σ(x)σt (x) and h(x) are locally Lipschitz in x and both have quadratic growth, then the solution to the signal process (7) exists and satisfies E sup X(t) 2d k T <, for d = 1, 2,. (10) 0 t T Moreover, the measure valued solution to the Zakai equation is unique.

15 Moment Estimate Solvability of Zakai Equation Convergence of Solutions to Zakai Equation Lemma For any t 0 and p 2, if h(x n j (t)) g(t) with t 0 g(s) 2 ds < for each t > 0. (11) Then there exists a constant c t,p 1 such that Ẽ[(aj n (t))p ] c t,p 1, j = 1,, n, (12) where a n j (t) = 1 + m k=1 t iε a n j (s)hk (X n j (s))dy k s. (13)

16 Main Theorem Solvability of Zakai Equation Convergence of Solutions to Zakai Equation Define the F t -adapted random variable ψ n = {ψt n, t 0} by [t/ε] ψt n 1 n := ( a n,iε n j )( 1 n aj n (t)). (14) n Let ρ n = {ρ n t, defined by i=1 j=1 j=1 t 0} be the measure-valued process ρ n t := ψn [t/ε]ε n n j=1 a n j (t)δ X n j (t) (15) ρ n t approximates the solution to the Zakai equation ρ t and formula (15) is the approximation of Kallianpur-Striebel formula.

17 Main Theorem Solvability of Zakai Equation Convergence of Solutions to Zakai Equation Define the F t -adapted random variable ψ n = {ψt n, t 0} by [t/ε] ψt n 1 n := ( a n,iε n j )( 1 n aj n (t)). (14) n Let ρ n = {ρ n t, defined by i=1 j=1 j=1 t 0} be the measure-valued process ρ n t := ψn [t/ε]ε n n j=1 a n j (t)δ X n j (t) (15) ρ n t approximates the solution to the Zakai equation ρ t and formula (15) is the approximation of Kallianpur-Striebel formula.

18 Main Theorem Solvability of Zakai Equation Convergence of Solutions to Zakai Equation Define the F t -adapted random variable ψ n = {ψt n, t 0} by [t/ε] ψt n 1 n := ( a n,iε n j )( 1 n aj n (t)). (14) n Let ρ n = {ρ n t, defined by i=1 j=1 j=1 t 0} be the measure-valued process ρ n t := ψn [t/ε]ε n n j=1 a n j (t)δ X n j (t) (15) ρ n t approximates the solution to the Zakai equation ρ t and formula (15) is the approximation of Kallianpur-Striebel formula.

19 Main Theorem Solvability of Zakai Equation Convergence of Solutions to Zakai Equation Theorem If the coefficients σ and f are globally Lipschitz and have finite initial data. h satisfies the condition in the previous lemma. Then for any T 0, there exists a constant c3 T independent of n such that for any positive φ C b (R 2n ), we have Ẽ[(ρ n t (φ) ρ t (φ)) 2 ] ct 3 n φ 2, t [0, T ]. (16) In particular, for all t 0, ρ n t converges in expectation to ρ t.

20 Unique solvability of the Zakai equation is obtained for unbounded h. Particle filter convergence of Zakai equation is generalized for h with deterministic L 2 functional bound. Outlook Stochastic vortex model could be analyzed on some Riemannian manifold(i.e. sphere), where coefficients of the model have nice properties. It is interesting to analyze the regularized kernel as ɛ approaches 0 and find its relation with the singular kernel in the Euler equation.

21 Unique solvability of the Zakai equation is obtained for unbounded h. Particle filter convergence of Zakai equation is generalized for h with deterministic L 2 functional bound. Outlook Stochastic vortex model could be analyzed on some Riemannian manifold(i.e. sphere), where coefficients of the model have nice properties. It is interesting to analyze the regularized kernel as ɛ approaches 0 and find its relation with the singular kernel in the Euler equation.

22 Unique solvability of the Zakai equation is obtained for unbounded h. Particle filter convergence of Zakai equation is generalized for h with deterministic L 2 functional bound. Outlook Stochastic vortex model could be analyzed on some Riemannian manifold(i.e. sphere), where coefficients of the model have nice properties. It is interesting to analyze the regularized kernel as ɛ approaches 0 and find its relation with the singular kernel in the Euler equation.

23 Unique solvability of the Zakai equation is obtained for unbounded h. Particle filter convergence of Zakai equation is generalized for h with deterministic L 2 functional bound. Outlook Stochastic vortex model could be analyzed on some Riemannian manifold(i.e. sphere), where coefficients of the model have nice properties. It is interesting to analyze the regularized kernel as ɛ approaches 0 and find its relation with the singular kernel in the Euler equation.

24 Unique solvability of the Zakai equation is obtained for unbounded h. Particle filter convergence of Zakai equation is generalized for h with deterministic L 2 functional bound. Outlook Stochastic vortex model could be analyzed on some Riemannian manifold(i.e. sphere), where coefficients of the model have nice properties. It is interesting to analyze the regularized kernel as ɛ approaches 0 and find its relation with the singular kernel in the Euler equation.

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26 Thank You!