18 research outputs found
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
Auto-regressive model based polarimetric adaptive detection scheme part I: Theoretical derivation and performance analysis
This paper deals with the problem of target detection in coherent radar systems exploiting polarimetric diversity. We resort to a parametric approach and we model the disturbance affecting the data as a multi-channel autoregressive (AR) process. Following this model, a new polarimetric adaptive detector is derived, which aims at improving the target detection capability while relaxing the requirements on the training data size and the computational burden with respect to existing solutions. A complete theoretical characterization of the asymptotic performance of the derived detector is provided, using two different target fluctuation models. The effectiveness of the proposed approach is shown against simulated data, in comparison with alternative existing solutions
Classification Schemes for the Radar Reference Window: Design and Comparisons
In this paper, we address the problem of classifying data within the radar
reference window in terms of statistical properties. Specifically, we partition
these data into statistically homogeneous subsets by identifying possible
clutter power variations with respect to the cells under test (accounting for
possible range-spread targets) and/or clutter edges. To this end, we consider
different situations of practical interest and formulate the classification
problem as multiple hypothesis tests comprising several models for the
operating scenario. Then, we solve the hypothesis testing problems by resorting
to suitable approximations of the model order selection rules due to the
intractable mathematics associated with the maximum likelihood estimation of
some parameters. Remarkably, the classification results provided by the
proposed architectures represent an advanced clutter map since, besides the
estimation of the clutter parameters, they contain a clustering of the range
bins in terms of homogeneous subsets. In fact, such information can drive the
conventional detectors towards more reliable estimates of the clutter
covariance matrix according to the position of the cells under test. The
performance analysis confirms that the conceived architectures represent a
viable means to recognize the scenario wherein the radar is operating at least
for the considered simulation parameters.Comment: Accepted by IEEE Transactions on Aerospace and Electronic System
Learning Strategies for Radar Clutter Classification
In this paper, we address the problem of classifying clutter returns in order
to partition them into statistically homogeneous subsets. The classification
procedure relies on a model for the observables including latent variables that
is solved by the expectation-maximization algorithm. The derivations are
carried out by accounting for three different cases for the structure of the
clutter covariance matrix. A preliminary performance analysis highlights that
the proposed technique is a viable means to cluster clutter returns over the
range.Comment: 12 pages, 13 figure
Design of Customized Adaptive Radar Detectors in the CFAR Feature Plane
The paper addresses the design of adaptive radar detectors with desired behavior, in Gaussian disturbance with unknown statistics. Specifically, based on detection probability specifications for chosen signal-to-noise ratios and steering vector mismatch levels, a methodology for the design of customized constant false alarm rate (CFAR) detectors is devised in a suitable feature plane obtained from two maximal invariant statistics. To overcome the analytical and numerical intractability of the resulting optimization problem, a novel general reduced-complexity algorithm is developed, which is shown to be effective in providing a feasible solution (i.e., fulfilling a constraint on the probability of false alarm) while controlling the behavior under both matched and mismatched conditions, so enabling the design of fully customized adaptive CFAR detectors
Wideband Spectrum Sensing for Dynamic Spectrum Sharing
The proliferation of wireless devices grows exponentially, demanding more and more data
communication capacity over wireless links. Radio spectrum is a scarce resource, and traditional
wireless networks deployed by Mobile Network Operators (MNO) are based on an exclusive
spectrum band allocation. However, underutilization of some licensed bands in time and geographic
domains has been reported, especially in rural areas or areas away from high population density
zones. This coexistence of increasingly high data communication needs and spectrum
underutilization is an incomprehensible scenario. A more rational and efficient use of the spectrum
is the possibility of Licensed Users (known as Primary Users – PU) to lease the spectrum, when
not in use, to Unlicensed Users (known as Secondary Users – SU), or allowing the SU to
opportunistically use the spectrum after sensing and verifying that the PU is idle. In this latter
case, the SU must stop transmitting when the PU becomes active.
This thesis addresses the spectrum sensing task, which is essential to provide dynamic spectrum
sharing between PUs and SUs. We show that the Spectral Correlation Function (SCF) and the
Spectral Coherence Function (SCoF) can provide a robust signal detection algorithm by exploiting
the cyclostationary characteristics of the data communication signal. We enhance the most used
algorithm to compute de SCF - the FAM (FFT Accumulation Method) algorithm – to efficiently
compute the SCF in a local/zoomed region of the support ( ; ) plane (frequency/cycle frequency
plane). This will provide the quick identification of spectral bands in use by PUs or free, in a
wideband sampling scenario.
Further, the characterization of the probability density of the estimates of the SCF and SCoF
when only noise is present, using the FAM algorithm, will allow the definition of an adaptive
threshold to develop a blind (with respect to the noise statistics) Constant False Alarm Rate
(CFAR) detector (using the SCoF) and also a CFAR and a Constant Detection Rate (CDR)
detector when that characterization is used to obtain an estimate of the background noise variance
(using the SCF).A proliferação de dispositivos sem fios cresce de forma exponencial, exigindo cada vez mais
capacidade de comunicação de dados através de ligações sem fios. O espectro radioelétrico é um
recurso escasso, e as redes sem fios tradicionais implantadas pelos Operadores de Redes Móveis
baseiam-se numa atribuição exclusiva de bandas do espectro. No entanto, tem sido relatada a
subutilização de algumas bandas licenciadas quer ao longo do tempo, quer na sua localização
geográfica, especialmente em áreas rurais, e em áreas longe de zonas de elevada densidade
populacional. A coexistência da necessidade cada vez maior de comunicação de dados, e a
subutilização do espectro é um cenário incompreensÃvel. Uma utilização mais racional e eficiente
do espectro pressupõe a possibilidade dos Utilizadores Licenciados (conhecidos como Utilizadores
Primários – Primary Users - PU) alugarem o espectro, quando este não está a ser utilizado, a
Utilizadores Não Licenciados (conhecidos como Utilizadores Secundários – Secondary Users - SU),
ou permitir ao SU utilizar oportunisticamente o espectro após a deteção e verificação de que o PU
está inativo. Neste último caso, o SU deverá parar de transmitir quando o PU ficar ativo.
Nesta tese é abordada a tarefa de deteção espectral, que é essencial para proporcionar a partilha
dinâmica do espectro entre PUs e SUs. Mostra-se que a Função de Correlação Espectral (Spectral
Correlation Function - SCF) e a Função de Coerência Espectral (Spectral Coherence Function -
SCoF) permitem o desenvolvimento de um algoritmo robusto de deteção de sinal, explorando as
caracterÃsticas ciclo-estacionárias dos sinais de comunicação de dados. Propõe-se uma melhoria ao
algoritmo mais utilizado para cálculo da SCF – o método FAM (FFT Accumulation Method) -
para permitir o cálculo mais eficiente da SCF numa região local/ampliada do plano de suporte
/ (plano de frequência/frequência de ciclo). Esta melhoria permite a identificação rápida de
bandas espectrais em uso por PUs ou livres, num cenário de amostragem de banda larga.
Adicionalmente, é feita a caracterização da densidade de probabilidade das estimativas da SCF e
SCoF quando apenas o ruÃdo está presente, o que permite a definição de um limiar adaptativo,
para desenvolver um detetor de Taxa de Falso Alarme Constante (Constant False Alarm Rate –
CFAR) sem conhecimento do ruÃdo de fundo (usando a SCoF) e também um detetor CFAR e Taxa
de Deteção Constante (Constant Detection Rate – CDR), quando se utiliza aquela caracterização
para obter uma estimativa da variância do ruÃdo de fundo (usando a SCF)
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion