70 research outputs found
A novel approach to robust radar detection of range-spread targets
This paper proposes a novel approach to robust radar detection of
range-spread targets embedded in Gaussian noise with unknown covariance matrix.
The idea is to model the useful target echo in each range cell as the sum of a
coherent signal plus a random component that makes the signal-plus-noise
hypothesis more plausible in presence of mismatches. Moreover, an unknown power
of the random components, to be estimated from the observables, is inserted to
optimize the performance when the mismatch is absent. The generalized
likelihood ratio test (GLRT) for the problem at hand is considered. In
addition, a new parametric detector that encompasses the GLRT as a special case
is also introduced and assessed. The performance assessment shows the
effectiveness of the idea also in comparison to natural competitors.Comment: 28 pages, 8 figure
Model Order Selection Rules For Covariance Structure Classification
The adaptive classification of the interference covariance matrix structure
for radar signal processing applications is addressed in this paper. This
represents a key issue because many detection architectures are synthesized
assuming a specific covariance structure which may not necessarily coincide
with the actual one due to the joint action of the system and environment
uncertainties. The considered classification problem is cast in terms of a
multiple hypotheses test with some nested alternatives and the theory of Model
Order Selection (MOS) is exploited to devise suitable decision rules. Several
MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria
are adopted and the corresponding merits and drawbacks are discussed. At the
analysis stage, illustrating examples for the probability of correct model
selection are presented showing the effectiveness of the proposed rules
Foundational principles for large scale inference: Illustrations through correlation mining
When can reliable inference be drawn in the "Big Data" context? This paper
presents a framework for answering this fundamental question in the context of
correlation mining, with implications for general large scale inference. In
large scale data applications like genomics, connectomics, and eco-informatics
the dataset is often variable-rich but sample-starved: a regime where the
number of acquired samples (statistical replicates) is far fewer than the
number of observed variables (genes, neurons, voxels, or chemical
constituents). Much of recent work has focused on understanding the
computational complexity of proposed methods for "Big Data." Sample complexity
however has received relatively less attention, especially in the setting when
the sample size is fixed, and the dimension grows without bound. To
address this gap, we develop a unified statistical framework that explicitly
quantifies the sample complexity of various inferential tasks. Sampling regimes
can be divided into several categories: 1) the classical asymptotic regime
where the variable dimension is fixed and the sample size goes to infinity; 2)
the mixed asymptotic regime where both variable dimension and sample size go to
infinity at comparable rates; 3) the purely high dimensional asymptotic regime
where the variable dimension goes to infinity and the sample size is fixed.
Each regime has its niche but only the latter regime applies to exa-scale data
dimension. We illustrate this high dimensional framework for the problem of
correlation mining, where it is the matrix of pairwise and partial correlations
among the variables that are of interest. We demonstrate various regimes of
correlation mining based on the unifying perspective of high dimensional
learning rates and sample complexity for different structured covariance models
and different inference tasks
Target Detection Architecture for Resource Constrained Wireless Sensor Networks within Internet of Things
Wireless sensor networks (WSN) within Internet of Things (IoT) have the potential
to address the growing detection and classi�cation requirements among many
surveillance applications. RF sensing techniques are the next generation technologies
which o�er distinct advantages over traditional passive means of sensing
such as acoustic and seismic which are used for surveillance and target detection
applications of WSN. RF sensing based WSN within IoT detect the presence of
designated targets by transmitting RF signals into the sensing environment and
observing the re
ected echoes. In this thesis, an RF sensing based target detection
architecture for surveillance applications of WSN has been proposed to detect the
presence of stationary targets within the sensing environment.
With multiple sensing nodes operating simultaneously within the sensing region,
diversity among the sensing nodes in the choice of transmit waveforms is required.
Existing multiple access techniques to accommodate multiple sensing nodes within
the sensing environment are not suitable for RF sensing based WSN. In this thesis,
a diversity in the choice of the transmit waveforms has been proposed and transmit
waveforms which are suitable for RF sensing based WSN have been discussed. A
criterion have been de�ned to quantify the ease of detecting the signal and energy
e�ciency of the signal based on which ease of detection index and energy e�ciency
index respectively have been generated. The waveform selection criterion proposed
in this thesis takes the WSN sensing conditions into account and identi�es the
optimum transmit waveform within the available choices of transmit waveforms
based on their respective ease of detection and energy e�ciency indexes.
A target detector analyses the received RF signals to make a decision regarding
the existence or absence of targets within the sensing region. Existing target detectors
which are discussed in the context of WSN do not take the factors such
as interference and nature of the sensing environment into account. Depending
on the nature of the sensing environment, in this thesis the sensing environments are classi�ed as homogeneous and heterogeneous sensing environments. Within
homogeneous sensing environments the presence of interference from the neighbouring
sensing nodes is assumed. A target detector has been proposed for WSN
within homogeneous sensing environments which can reliably detect the presence
of targets. Within heterogeneous sensing environments the presence of clutter and
interfering waveforms is assumed. A target detector has been proposed for WSN
within heterogeneous sensing environments to detect targets in the presence of
clutter and interfering waveforms. A clutter estimation technique has been proposed
to assist the proposed target detector to achieve increased target detection
reliability in the presence of clutter. A combination of compressive and two-step
target detection architectures has been proposed to reduce the transmission costs.
Finally, a 2-stage target detection architecture has been proposed to reduce the
computational complexity of the proposed target detection architecture
Recent Advances in Wireless Communications and Networks
This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters
New Directions for Contact Integrators
Contact integrators are a family of geometric numerical schemes which
guarantee the conservation of the contact structure. In this work we review the
construction of both the variational and Hamiltonian versions of these methods.
We illustrate some of the advantages of geometric integration in the
dissipative setting by focusing on models inspired by recent studies in
celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282
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