相关论文: Filter design for the detection of compact sources…

In this paper we use the MAP criterion to locate a region containing a source. Sensors placed in a field of interest divide the latter into smaller regions and take measurements that are transmitted over noisy wireless channels. We propose…

In this study, a multiple-comparison approach is developed for detecting faint hyperspectral sources. The detection method relies on a sparse and non-negative representation on a highly coherent dictionary to track a spatially varying…

Digital sensors can lead to noisy results under many circumstances. To be able to remove the undesired noise from images, proper noise modeling and an accurate noise parameter estimation is crucial. In this project, we use a…

Detection of a signal under noise is a classical signal processing problem. When monitoring spatial phenomena under a fixed budget, i.e., either physical, economical or computational constraints, the selection of a subset of available…

We consider a multi-stage distributed detection scenario, where $n$ sensors and a fusion center (FC) are deployed to accomplish a binary hypothesis test. At each time stage, local sensors generate binary messages, assumed to be spatially…

The search for gravitational-wave signals in detector data is often hampered by the fact that many data analysis methods are based on the theory of stationary Gaussian noise, while actual measurement data frequently exhibit clear departures…

We consider the non-parametric Poisson regression problem where the integer valued response $Y$ is the realization of a Poisson random variable with parameter $\lambda(X)$. The aim is to estimate the functional parameter $\lambda$ from…

We analyze the distinguishability of two sources in a Neyman-Pearson set-up when an attacker is allowed to modify the output of one of the two sources subject to a distortion constraint. By casting the problem in a game-theoretic framework…

This paper introduces a Bayesian framework to detect multiple signals embedded in noisy observations from a sensor array. For various states of knowledge on the communication channel and the noise at the receiving sensors, a marginalization…

We consider a Neyman-Pearson (NP) distributed binary detection problem in a bandwidth constrained wireless sensor network, where the fusion center (FC) is responsible for fusing signals received from sensors and making a final decision…

We consider estimation of a deterministic unknown parameter vector in a linear model with non-Gaussian noise. In the Gaussian case, dimensionality reduction via a linear matched filter provides a simple low dimensional sufficient statistic…

Procedures based on current methods to detect sources in X-ray images are applied to simulated XMM images. All significant instrumental effects are taken into account, and two kinds of sources are considered -- unresolved sources…

In this work we address the problem of simultaneous multi-frequency detection of extragalactic point sources in maps of the Cosmic Microwave Background. We apply a new linear filtering technique, the so called `matched matrix filters', that…

The detection of hidden two-dimensional Gauss-Markov random fields using sensor networks is considered. Under a conditional autoregressive model, the error exponent for the Neyman-Pearson detector satisfying a fixed level constraint is…

An introduction to the theory of sliding window detection processes, used as alternatives to optimal Neyman-Pearson based radar detectors, is presented. Included is an outline of their historical development, together with an explanation…

We describe the method used to detect sources for the Herschel-ATLAS survey. The method is to filter the individual bands using a matched filter, based on the point-spread function (PSF) and confusion noise, and then form the inverse…

Gravitational wave detectors now under construction are sensitive to the phase of the incident gravitational waves. Correspondingly, the signals from the different detectors can be combined, in the analysis, to simulate a single detector of…

We derive optimal filters on the sphere in the context of detecting compact objects embedded in a stochastic background process. The matched filter and the scale adaptive filter are derived on the sphere in the most general setting,…

A Bayesian approach is presented for detecting and characterising the signal from discrete objects embedded in a diffuse background. The approach centres around the evaluation of the posterior distribution for the parameters of the discrete…

Feature selection aims to select the smallest subset of features for a specified level of performance. The optimal achievable classification performance on a feature subset is summarized by its Receiver Operating Curve (ROC). When infinite…