Imaging of obscured targets in random media is a difficult and important problem. One of the central questions is that of stability which is particularly relevant to imaging in stochastic media. The main goal of the research is to develop a general criterion for multiple-frequency array imaging of multiple targets in stochastic media. An important feature of the cluttered media we considered here is that the coherent or mean signals do not vanish. It is called Rician fading medium in communication. Foldy-Lax formulas are used to simulate the exact wave propagations in the random media. In the talk, I will propose two models: passive array model and active array model. Then the stabilities of the imaging function with multiple point targets are given in both cases, followed by the numerical simulations to show the consistency with the analysis. If time permits, I will give some preliminary results about the stability conditions of the multiple extended targets, as well as the simulations.
We consider a directed polymer pinned by one-dimensional quenched randomness, modeled by the space-time trajectories of an underlying Markov chain which encounters a random potential of form u + V_i when it visits a particular site, denoted 0, at time i. The polymer depins from the potential when u goes below a critical value. We consider in particular the case in which the excursion length (from 0) of the underlying Markov chain has power law tails. We show that for certain tail exponents, for small inverse temperature \beta there is a constant D(\beta), approaching 0 with \beta, such that if the increment of u above the annealed critical point is a large multiple of D(\beta) then the quenched and annealed systems have very similar free energies, and are both pinned, but if the increment is a small multiple of D(\beta), the annealed system is pinned while the quenched is not. In other words, the breakdown of the ability of the quenched system to mimic the annealed occurs entirely at order D(\beta) above the annealed critical point.
We present a continuation pf work with Molchanov on the behavior of "random walk" oaths under a Gibbs measure which introduces an attraction to the origin with strength depending on a parameter b.
There is a phase transition from a transient or diffusive phase to a globular phase and we discuss behavior at and around the critical value of the parameter .
Consider a family of probability measures $\{\mu_y : y \in
\partial D\}$ on a bounded open domain $D\subset R^d$ with smooth
boundary.
For any starting point $x \in D$, we run a
a standard $d$-dimensional Brownian motion $B(t) \in R^d $ until it first
exits $D$ at time $\tau$,
at which time it jumps to a point in the domain $D$ according to the
measure $\mu_{B(\tau)}$ at the exit time,
and starts the Brownian motion afresh. The same evolution is repeated
independently each time the process reaches the boundary.
The resulting diffusion process is called Brownian motion with jump
boundary (BMJ).
The spectral gap of non-self-adjoint generator of BMJ, which describes the
exponential
rate of convergence to the invariant measure, is studied.
The main analytic tool is Fourier transforms with only real zeros.
I will discuss the behavior of N bosons trapped in a potential well subject
to a pairwise interaction. In the mean-field limit -- i.e., when N tends to
infinity while keeping the interaction per particle bounded -- the evolution
of a product state remains, asymptotically, a product state. The single
particle wave-function then evolves according to a non-linear Hartree
equation. Versions of this result have been proved before, e.g., by
Hepp in 1977, Spohn in 1980 or, recently, by Rodnianski and Schlein, but
the proofs are often quite technically involved. I will describe a very simple,
and ideologically correct, proof (for bounded interaction potentials) based
on exchengeability and Stormer's (aka quantum deFinetti) theorem.
Based on recent discussions with Nick Crawford.
The two main approaches to modeling defaults, structural and intensity based, will be reviewed. We show that perturbation methods are useful in approximating default probabilities in the context of stochastic volatility models. We then consider the case of many names and we discuss various ways of creating correlation of defaults. In highly-dimensional models, Monte Carlo simulations remain a powerful tool for computing prices of credit derivatives such as CDO's tranches and associated greeks. We propose an interactive particle system approach for computing the small probabilities involved in these financial instruments.