In this paper, radar detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like the GLRT method). In the sec...

A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian autocorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown...

Herein we investigate the problem of detecting and localizing a known signal in a photon-limited image, where Poisson noise is the dominant source of image degradation. For this purpose we developed and evaluated three new algorithms. The first two are based on the impulse restoration (IR) principle and the third is based on the generalized likelihood ratio test (GLRT). In the IR approach, the ...

We consider the problem of detecting anisotropic scattering of targets from wideband SAR measurements. We rst develop a scattering model for the response of an ideal dihedral when interrogated by a wideband radar. We formulate a stochastic detection problem based on this model and Gaussian clutter models. We investigate the performance of three detectors, the conventional imaging detector, a ge...

In this paper, we propose a novel mechanism for spectrum sensing that leads us to exploit the spatio-temporal correlation present in the received signal at a multi-antenna receiver. For the proposed mechanism, we formulate the spectrum sensing scheme by adopting the generalized likelihood ratio test (GLRT). However, the GLRT degenerates in the case of limited sample support. To circumvent this ...

This paper proposes a novel methodology to detect data hidden in the least significant bits of a natural image. The goal is twofold: first, the methodology aims at proposing a test specifically designed for natural images, to this end an original model of images is presented, and, second, the statistical properties of the designed test, probability of false alarm and power function, should be p...

The separation of a complex mixture based solely on second-order statistics can be achieved using the Strong Uncorrelating Transform (SUT) if and only if all sources have distinct circularity coefficients. However, in most problems we do not know the circularity coefficients, and they must be estimated from observed data. In this work, we propose a detector, based on the generalized likelihood ...

We propose a method to detect a target in a bistatic passive polarimetric radar network, with weather surveillance radar as our illuminator of opportunity (IO). We build our signal model using electromagnetic vector sensors (EMVS) as the receiver, which captures the reflections from a point-like target present in the scene of interest, surrounded with strong clutter. We develop a generalized li...

We investigate the problem of detecting and localizing a known signal in a photon-limited image, where Poisson noise is the dominant source of image degradation. For this purpose we developed and evaluated three new algorithms. The first two are based on the impulse restoration (IR) principle and the third is based on the generalized likelihood ratio test (GLRT). In the IR approach, the problem...

This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of t...