10,742 research outputs found
Sampling and Reconstruction of Spatial Fields using Mobile Sensors
Spatial sampling is traditionally studied in a static setting where static
sensors scattered around space take measurements of the spatial field at their
locations. In this paper we study the emerging paradigm of sampling and
reconstructing spatial fields using sensors that move through space. We show
that mobile sensing offers some unique advantages over static sensing in
sensing time-invariant bandlimited spatial fields. Since a moving sensor
encounters such a spatial field along its path as a time-domain signal, a
time-domain anti-aliasing filter can be employed prior to sampling the signal
received at the sensor. Such a filtering procedure, when used by a
configuration of sensors moving at constant speeds along equispaced parallel
lines, leads to a complete suppression of spatial aliasing in the direction of
motion of the sensors. We analytically quantify the advantage of using such a
sampling scheme over a static sampling scheme by computing the reduction in
sampling noise due to the filter. We also analyze the effects of non-uniform
sensor speeds on the reconstruction accuracy. Using simulation examples we
demonstrate the advantages of mobile sampling over static sampling in practical
problems.
We extend our analysis to sampling and reconstruction schemes for monitoring
time-varying bandlimited fields using mobile sensors. We demonstrate that in
some situations we require a lower density of sensors when using a mobile
sensing scheme instead of the conventional static sensing scheme. The exact
advantage is quantified for a problem of sampling and reconstructing an audio
field.Comment: Submitted to IEEE Transactions on Signal Processing May 2012; revised
Oct 201
Sub-Nyquist Channel Estimation over IEEE 802.11ad Link
Nowadays, millimeter-wave communication centered at the 60 GHz radio
frequency band is increasingly the preferred technology for near-field
communication since it provides transmission bandwidth that is several GHz
wide. The IEEE 802.11ad standard has been developed for commercial wireless
local area networks in the 60 GHz transmission environment. Receivers designed
to process IEEE 802.11ad waveforms employ very high rate analog-to-digital
converters, and therefore, reducing the receiver sampling rate can be useful.
In this work, we study the problem of low-rate channel estimation over the IEEE
802.11ad 60 GHz communication link by harnessing sparsity in the channel
impulse response. In particular, we focus on single carrier modulation and
exploit the special structure of the 802.11ad waveform embedded in the channel
estimation field of its single carrier physical layer frame. We examine various
sub-Nyquist sampling methods for this problem and recover the channel using
compressed sensing techniques. Our numerical experiments show feasibility of
our procedures up to one-seventh of the Nyquist rates with minimal performance
deterioration.Comment: 5 pages, 5 figures, SampTA 2017 conferenc
The On The Fly Imaging Technique
The On-The-Fly (OTF) imaging technique enables single-dish radio telescopes
to construct images of small areas of the sky with greater efficiency and
accuracy. This paper describes the practical application of the OTF imaging
technique. By way of example the implementation of the OTF imaging technique at
the NRAO 12 Meter Telescope is described. Specific requirements for data
sampling, image formation, and Doppler correction are discussed.Comment: 10 pages, 13 figures, accepted A&
Information Loss and Anti-Aliasing Filters in Multirate Systems
This work investigates the information loss in a decimation system, i.e., in
a downsampler preceded by an anti-aliasing filter. It is shown that, without a
specific signal model in mind, the anti-aliasing filter cannot reduce
information loss, while, e.g., for a simple signal-plus-noise model it can. For
the Gaussian case, the optimal anti-aliasing filter is shown to coincide with
the one obtained from energetic considerations. For a non-Gaussian signal
corrupted by Gaussian noise, the Gaussian assumption yields an upper bound on
the information loss, justifying filter design principles based on second-order
statistics from an information-theoretic point-of-view.Comment: 12 pages; a shorter version of this paper was published at the 2014
International Zurich Seminar on Communication
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