Henrik Ohlsson
Compressive Phase Retrieval From Squared Output Measurements Via Semidefinite Programming.
Tue, 11/29/2011 - 15:01 — SomNambulAuthors: S. Shankar Sastry, Henrik Ohlsson, Allen Y. Yang, Roy Dong
Subjects: Statistics
AbstractGiven a linear system in a real or complex domain, linear regression aims to
recover the model parameters from a set of observations. Recent studies in
compressive sensing have successfully shown that under certain conditions, a
linear program, namely, l1-minimization, guarantees recovery of sparse
parameter signals even when the system is underdetermined. In this paper, we
consider a more challenging problem: when the phase of the output measurements
from a linear system is omitted.A Probabilistic Perspective on Gaussian Filtering and Smoothing.
Mon, 06/14/2010 - 15:01 — SomNambulAuthors: Marc Peter Deisenroth, Henrik Ohlsson
Subjects: Methodology
AbstractWe present a general probabilistic perspective on Gaussian filtering and
smoothing. We show that different approaches to Gaussian filtering/smoothing
can be distinguished solely by their methods of computing means and covariances
of joint probabilities. New filters and smoothers can therefore be derived
easily by providing methods for computing these moments. From the probabilistic
perspective, we additionally derive general sufficient conditions for
unbiasedness and optimality of Gaussian filters in linear and nonlinear dynamic
systems.
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