51,466 research outputs found
Filter design for the detection of compact sources based on the Neyman-Pearson detector
This paper considers the problem of compact source detection on a Gaussian
background in 1D. Two aspects of this problem are considered: the design of the
detector and the filtering of the data. Our detection scheme is based on local
maxima and it takes into account not only the amplitude but also the curvature
of the maxima. A Neyman-Pearson test is used to define the region of
acceptance, that is given by a sufficient linear detector that is independent
on the amplitude distribution of the sources. We study how detection can be
enhanced by means of linear filters with a scaling parameter and compare some
of them (the Mexican Hat wavelet, the matched and the scale-adaptive filters).
We introduce a new filter, that depends on two free parameters (biparametric
scale-adaptive filter). The value of these two parameters can be determined,
given the a priori pdf of the amplitudes of the sources, such that the filter
optimizes the performance of the detector in the sense that it gives the
maximum number of real detections once fixed the number density of spurious
sources. The combination of a detection scheme that includes information on the
curvature and a flexible filter that incorporates two free parameters (one of
them a scaling) improves significantly the number of detections in some
interesting cases. In particular, for the case of weak sources embedded in
white noise the improvement with respect to the standard matched filter is of
the order of 40%. Finally, an estimation of the amplitude of the source is
introduced and it is proven that such an estimator is unbiased and it has
maximum efficiency. We perform numerical simulations to test these theoretical
ideas and conclude that the results of the simulations agree with the
analytical ones.Comment: 15 pages, 13 figures, version accepted for publication in MNRAS.
Corrected typos in Tab.
MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter
Multiple-input multiple-output (MIMO) radar has become a thriving subject of
research during the past decades. In the MIMO radar context, it is sometimes
more accurate to model the radar clutter as a non-Gaussian process, more
specifically, by using the spherically invariant random process (SIRP) model.
In this paper, we focus on the estimation and performance analysis of the
angular spacing between two targets for the MIMO radar under the SIRP clutter.
First, we propose an iterative maximum likelihood as well as an iterative
maximum a posteriori estimator, for the target's spacing parameter estimation
in the SIRP clutter context. Then we derive and compare various
Cram\'er-Rao-like bounds (CRLBs) for performance assessment. Finally, we
address the problem of target resolvability by using the concept of angular
resolution limit (ARL), and derive an analytical, closed-form expression of the
ARL based on Smith's criterion, between two closely spaced targets in a MIMO
radar context under SIRP clutter. For this aim we also obtain the non-matrix,
closed-form expressions for each of the CRLBs. Finally, we provide numerical
simulations to assess the performance of the proposed algorithms, the validity
of the derived ARL expression, and to reveal the ARL's insightful properties.Comment: 34 pages, 12 figure
Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data
Context. The direct detection of exoplanets with high-contrast imaging
requires advanced data processing methods to disentangle potential planetary
signals from bright quasi-static speckles. Among them, angular differential
imaging (ADI) permits potential planetary signals with a known rotation rate to
be separated from instrumental speckles that are either statics or slowly
variable. The method presented in this paper, called ANDROMEDA for ANgular
Differential OptiMal Exoplanet Detection Algorithm is based on a maximum
likelihood approach to ADI and is used to estimate the position and the flux of
any point source present in the field of view. Aims. In order to optimize and
experimentally validate this previously proposed method, we applied ANDROMEDA
to real VLT/NaCo data. In addition to its pure detection capability, we
investigated the possibility of defining simple and efficient criteria for
automatic point source extraction able to support the processing of large
surveys. Methods. To assess the performance of the method, we applied ANDROMEDA
on VLT/NaCo data of TYC-8979-1683-1 which is surrounded by numerous bright
stars and on which we added synthetic planets of known position and flux in the
field. In order to accommodate the real data properties, it was necessary to
develop additional pre-processing and post-processing steps to the initially
proposed algorithm. We then investigated its skill in the challenging case of a
well-known target, Pictoris, whose companion is close to the detection
limit and we compared our results to those obtained by another method based on
principal component analysis (PCA). Results. Application on VLT/NaCo data
demonstrates the ability of ANDROMEDA to automatically detect and characterize
point sources present in the image field. We end up with a robust method
bringing consistent results with a sensitivity similar to the recently
published algorithms, with only two parameters to be fine tuned. Moreover, the
companion flux estimates are not biased by the algorithm parameters and do not
require a posteriori corrections. Conclusions. ANDROMEDA is an attractive
alternative to current standard image processing methods that can be readily
applied to on-sky data
Bayesian methods of astronomical source extraction
We present two new source extraction methods, based on Bayesian model
selection and using the Bayesian Information Criterion (BIC). The first is a
source detection filter, able to simultaneously detect point sources and
estimate the image background. The second is an advanced photometry technique,
which measures the flux, position (to sub-pixel accuracy), local background and
point spread function. We apply the source detection filter to simulated
Herschel-SPIRE data and show the filter's ability to both detect point sources
and also simultaneously estimate the image background. We use the photometry
method to analyse a simple simulated image containing a source of unknown flux,
position and point spread function; we not only accurately measure these
parameters, but also determine their uncertainties (using Markov-Chain Monte
Carlo sampling). The method also characterises the nature of the source
(distinguishing between a point source and extended source). We demonstrate the
effect of including additional prior knowledge. Prior knowledge of the point
spread function increase the precision of the flux measurement, while prior
knowledge of the background has onlya small impact. In the presence of higher
noise levels, we show that prior positional knowledge (such as might arise from
a strong detection in another waveband) allows us to accurately measure the
source flux even when the source is too faint to be detected directly. These
methods are incorporated in SUSSEXtractor, the source extraction pipeline for
the forthcoming Akari FIS far-infrared all-sky survey. They are also
implemented in a stand-alone, beta-version public tool that can be obtained at
http://astronomy.sussex.ac.uk/rss23/sourceMiner\_v0.1.2.0.tar.gzComment: Accepted for publication by ApJ (this version compiled used
emulateapj.cls
Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments
We address the problem of online localization and tracking of multiple moving
speakers in reverberant environments. The paper has the following
contributions. We use the direct-path relative transfer function (DP-RTF), an
inter-channel feature that encodes acoustic information robust against
reverberation, and we propose an online algorithm well suited for estimating
DP-RTFs associated with moving audio sources. Another crucial ingredient of the
proposed method is its ability to properly assign DP-RTFs to audio-source
directions. Towards this goal, we adopt a maximum-likelihood formulation and we
propose to use an exponentiated gradient (EG) to efficiently update
source-direction estimates starting from their currently available values. The
problem of multiple speaker tracking is computationally intractable because the
number of possible associations between observed source directions and physical
speakers grows exponentially with time. We adopt a Bayesian framework and we
propose a variational approximation of the posterior filtering distribution
associated with multiple speaker tracking, as well as an efficient variational
expectation-maximization (VEM) solver. The proposed online localization and
tracking method is thoroughly evaluated using two datasets that contain
recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201
Improving and Assessing Planet Sensitivity of the GPI Exoplanet Survey with a Forward Model Matched Filter
We present a new matched filter algorithm for direct detection of point
sources in the immediate vicinity of bright stars. The stellar Point Spread
Function (PSF) is first subtracted using a Karhunen-Lo\'eve Image Processing
(KLIP) algorithm with Angular and Spectral Differential Imaging (ADI and SDI).
The KLIP-induced distortion of the astrophysical signal is included in the
matched filter template by computing a forward model of the PSF at every
position in the image. To optimize the performance of the algorithm, we conduct
extensive planet injection and recovery tests and tune the exoplanet spectra
template and KLIP reduction aggressiveness to maximize the Signal-to-Noise
Ratio (SNR) of the recovered planets. We show that only two spectral templates
are necessary to recover any young Jovian exoplanets with minimal SNR loss. We
also developed a complete pipeline for the automated detection of point source
candidates, the calculation of Receiver Operating Characteristics (ROC), false
positives based contrast curves, and completeness contours. We process in a
uniform manner more than 330 datasets from the Gemini Planet Imager Exoplanet
Survey (GPIES) and assess GPI typical sensitivity as a function of the star and
the hypothetical companion spectral type. This work allows for the first time a
comparison of different detection algorithms at a survey scale accounting for
both planet completeness and false positive rate. We show that the new forward
model matched filter allows the detection of fainter objects than a
conventional cross-correlation technique with a Gaussian PSF template for the
same false positive rate.Comment: ApJ accepte
Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform
The Hilbert-Huang Transform is a novel, adaptive approach to time series
analysis that does not make assumptions about the data form. Its adaptive,
local character allows the decomposition of non-stationary signals with
hightime-frequency resolution but also renders it susceptible to degradation
from noise. We show that complementing the HHT with techniques such as
zero-phase filtering, kernel density estimation and Fourier analysis allows it
to be used effectively to detect and characterize signals with low signal to
noise ratio.Comment: submitted to PRD, 10 pages, 9 figures in colo
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