21 research outputs found
Accuracy of spike-train Fourier reconstruction for colliding nodes
We consider Fourier reconstruction problem for signals F, which are linear
combinations of shifted delta-functions. We assume the Fourier transform of F
to be known on the frequency interval [-N,N], with an absolute error not
exceeding e > 0. We give an absolute lower bound (which is valid with any
reconstruction method) for the "worst case" reconstruction error of F in
situations where the nodes (i.e. the positions of the shifted delta-functions
in F) are known to form an l elements cluster of a size h << 1. Using
"decimation" reconstruction algorithm we provide an upper bound for the
reconstruction error, essentially of the same form as the lower one. Roughly,
our main result states that for N*h of order of (2l-1)-st root of e the worst
case reconstruction error of the cluster nodes is of the same order as h, and
hence the inside configuration of the cluster nodes (in the worst case
scenario) cannot be reconstructed at all. On the other hand, decimation
algorithm reconstructs F with the accuracy of order of 2l-st root of e
Super-Resolution in Phase Space
This work considers the problem of super-resolution. The goal is to resolve a
Dirac distribution from knowledge of its discrete, low-pass, Fourier
measurements. Classically, such problems have been dealt with parameter
estimation methods. Recently, it has been shown that convex-optimization based
formulations facilitate a continuous time solution to the super-resolution
problem. Here we treat super-resolution from low-pass measurements in Phase
Space. The Phase Space transformation parametrically generalizes a number of
well known unitary mappings such as the Fractional Fourier, Fresnel, Laplace
and Fourier transforms. Consequently, our work provides a general super-
resolution strategy which is backward compatible with the usual Fourier domain
result. We consider low-pass measurements of Dirac distributions in Phase Space
and show that the super-resolution problem can be cast as Total Variation
minimization. Remarkably, even though are setting is quite general, the bounds
on the minimum separation distance of Dirac distributions is comparable to
existing methods.Comment: 10 Pages, short paper in part accepted to ICASSP 201
Accuracy of reconstruction of spike-trains with two near-colliding nodes
We consider a signal reconstruction problem for signals of the form from their moments
We assume to be known for
with an absolute error not exceeding .
We study the "geometry of error amplification" in reconstruction of from
in situations where two neighboring nodes and
near-collide, i.e . We show that the error amplification
is governed by certain algebraic curves in the parameter space of
signals , along which the first three moments remain constant