18,573 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
On Minimal Trajectories for Mobile Sampling of Bandlimited Fields
We study the design of sampling trajectories for stable sampling and the
reconstruction of bandlimited spatial fields using mobile sensors. The spectrum
is assumed to be a symmetric convex set. As a performance metric we use the
path density of the set of sampling trajectories that is defined as the total
distance traveled by the moving sensors per unit spatial volume of the spatial
region being monitored. Focussing first on parallel lines, we identify the set
of parallel lines with minimal path density that contains a set of stable
sampling for fields bandlimited to a known set. We then show that the problem
becomes ill-posed when the optimization is performed over all trajectories by
demonstrating a feasible trajectory set with arbitrarily low path density.
However, the problem becomes well-posed if we explicitly specify the stability
margins. We demonstrate this by obtaining a non-trivial lower bound on the path
density of an arbitrary set of trajectories that contain a sampling set with
explicitly specified stability bounds.Comment: 28 pages, 8 figure
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
One-bit Distributed Sensing and Coding for Field Estimation in Sensor Networks
This paper formulates and studies a general distributed field reconstruction
problem using a dense network of noisy one-bit randomized scalar quantizers in
the presence of additive observation noise of unknown distribution. A
constructive quantization, coding, and field reconstruction scheme is developed
and an upper-bound to the associated mean squared error (MSE) at any point and
any snapshot is derived in terms of the local spatio-temporal smoothness
properties of the underlying field. It is shown that when the noise, sensor
placement pattern, and the sensor schedule satisfy certain weak technical
requirements, it is possible to drive the MSE to zero with increasing sensor
density at points of field continuity while ensuring that the per-sensor
bitrate and sensing-related network overhead rate simultaneously go to zero.
The proposed scheme achieves the order-optimal MSE versus sensor density
scaling behavior for the class of spatially constant spatio-temporal fields.Comment: Fixed typos, otherwise same as V2. 27 pages (in one column review
format), 4 figures. Submitted to IEEE Transactions on Signal Processing.
Current version is updated for journal submission: revised author list,
modified formulation and framework. Previous version appeared in Proceedings
of Allerton Conference On Communication, Control, and Computing 200
Efficient Compressive Sampling of Spatially Sparse Fields in Wireless Sensor Networks
Wireless sensor networks (WSN), i.e. networks of autonomous, wireless sensing
nodes spatially deployed over a geographical area, are often faced with
acquisition of spatially sparse fields. In this paper, we present a novel
bandwidth/energy efficient CS scheme for acquisition of spatially sparse fields
in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse,
structured CS matrix and we analytically show that it allows accurate
reconstruction of bidimensional spatially sparse signals, such as those
occurring in several surveillance application. Secondly, we analytically
evaluate the energy and bandwidth consumption of our CS scheme when it is
applied to data acquisition in a WSN. Numerical results demonstrate that our CS
scheme achieves significant energy and bandwidth savings wrt state-of-the-art
approaches when employed for sensing a spatially sparse field by means of a
WSN.Comment: Submitted to EURASIP Journal on Advances in Signal Processin
MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications
According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente
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