4 research outputs found
Bandlimited Spatial Field Sampling with Mobile Sensors in the Absence of Location Information
Sampling of physical fields with mobile sensor is an emerging area. In this
context, this work introduces and proposes solutions to a fundamental question:
can a spatial field be estimated from samples taken at unknown sampling
locations?
Unknown sampling location, sample quantization, unknown bandwidth of the
field, and presence of measurement-noise present difficulties in the process of
field estimation. In this work, except for quantization, the other three issues
will be tackled together in a mobile-sampling framework. Spatially bandlimited
fields are considered. It is assumed that measurement-noise affected field
samples are collected on spatial locations obtained from an unknown renewal
process. That is, the samples are obtained on locations obtained from a renewal
process, but the sampling locations and the renewal process distribution are
unknown. In this unknown sampling location setup, it is shown that the
mean-squared error in field estimation decreases as where is the
average number of samples collected by the mobile sensor. The average number of
samples collected is determined by the inter-sample spacing distribution in the
renewal process. An algorithm to ascertain spatial field's bandwidth is
detailed, which works with high probability as the average number of samples
increases. This algorithm works in the same setup, i.e., in the presence of
measurement-noise and unknown sampling locations.Comment: Submitted to IEEE Trans on Signal Processin
Universal Spatiotemporal Sampling Sets for Discrete Spatially Invariant Evolution Systems
Let be a finite abelian group and be a circular
convolution operator on . The problem under consideration is how to
construct minimal and such that is
a frame for , where is the canonical
basis of . This problem is motivated by the spatiotemporal sampling
problem in discrete spatially invariant evolution systems. We will show that
the cardinality of should be at least equal to the largest geometric
multiplicity of eigenvalues of , and we consider the universal
spatiotemporal sampling sets for convolution operators
with eigenvalues subject to the same largest geometric
multiplicity. We will give an algebraic characterization for such sampling sets
and show how this problem is linked with sparse signal processing theory and
polynomial interpolation theory
SAMPLING AND RECONSTRUCTING DIFFUSION FIELDS IN PRESENCE OF ALIASING
The reconstruction of a diffusion field, such as temperature, from samples collected by a sensor network is a classical inverse problem and it is known to be ill-conditioned. Previous work considered source models, such as sparse sources, to regularize the solution. Here, we consider uniform spatial sampling and reconstruction by classical interpolation techniques for those scenarios where the spatial sparsity of the sources is not realistic. We show that even if the spatial bandwidth of the field is infinite, we can exploit the natural lowpass filter given by the diffusion phenomenon to bound the aliasing error. Index Terms — Diffusion equation, initial inverse problems, spatial sampling, aliasing error, interpolation. 1