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Simultaneous observations by the large number of gamma-ray burst detectors operating in the GLAST era will provide the spectra, lightcurves and locations necessary for studying burst physics and testing the putative relations between…
With the improvement in the quantity and quality of remote sensing images, content-based remote sensing object retrieval (CBRSOR) has become an increasingly important topic. However, existing CBRSOR methods neglect the utilization of global…
In imaging atmospheric Cherenkov telescope (IACT) arrays, the standard method of statistically inferring the existence of a source is based on the maximum likelihood method of Li&Ma (1983). We present a new statistical approach, also based…
Geometric environment information aids future distributed radio infrastructures in providing services, such as ultra-reliable communication, positioning, and wireless power transfer (WPT). An a priori known environment model cannot always…
We investigate the behavior of the Generalized Likelihood Ratio Test (GLRT) (Fan, Zhang and Zhang [Ann. Statist. 29 (2001) 153-193]) for time varying coefficient models where the regressors and errors are non-stationary time series and can…
Besides atmospheric turbulence and pointing errors which cause the signal intensity fluctuation, background radiation also impairs the free-space optical intensity-modulation / direct-detection link performance by introducing a noisy…
Analyzing data from GLAST's Large Area Telescope (LAT) will require sophisticated techniques. The PSF and effective area are functions of both photon energy and the position in the field-of-view. During most of the mission the observatory…
(ABRIDGED) The Gamma-ray Large Area Space Telescope (GLAST) will measure the spectra of distant extragalactic sources of high energy gamma-rays. GLAST can look for energy dependent propagation effects from such sources as a signal of…
This paper studies recursive composite hypothesis testing in a network of sparsely connected agents. The network objective is to test a simple null hypothesis against a composite alternative concerning the state of the field, modeled as a…
In this paper, we address the problem of target detection in passive multiple-input-multiple-output (MIMO) radar networks. A generalized likelihood ratio test is derived, assuming prior knowledge of the signal format used in the…
This paper is devoted to the performance analysis of the detectors proposed in the companion paper where a comprehensive design framework is presented for the adaptive detection of subspace signals. The framework addresses four variations…
Based on the technique of generalized likelihood ratio test, we address detection schemes for Doppler-shifted range-spread targets in Gaussian noise. First, a detection scheme is derived by solving the maximization associated with the…
Crosscorrelation of the outputs of two Gravitational Wave (GW) detectors has recently been proposed [1] as a method for detecting statistical association between GWs and Gamma Ray Bursts (GRBs). Unfortunately, the method can be effectively…
Deep learning image classifiers are known to be vulnerable to small adversarial perturbations of input images. In this paper, we derive the locally optimal generalized likelihood ratio test (LO-GLRT) based detector for detecting stochastic…
Locating a target is key in many applications, namely in high-stakes real-world scenarios, like detecting humans or obstacles in vehicular networks. In scenarios where precise statistics of the measurement noise are unavailable,…
A variety of pulsar studies, ranging from high precision astrometry to tests for theories of gravity, require high time resolution data. Few such observations at more than two frequencies below 1 GHz are available. Giant Meterwave Radio…
This paper tackles the problem of finding the optimal non-coherent detector for the reacquisition of weak Global Navigation Satellite System (GNSS) signals in the presence of bits and phase uncertainty. Two solutions are derived based on…
We propose an analytical framework based on stochastic geometry (SG) formulations to estimate a radar's detection performance under generalized discrete clutter conditions. We model the spatial distribution of discrete clutter scatterers as…
The problem of linear modulation classification using likelihood based methods is considered. Asymptotic properties of most commonly used classifiers in the literature are derived. These classifiers are based on hybrid likelihood ratio test…
An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different…