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Ambient backscatter is an emerging technology that utilizes the surrounding radio frequency (RF) signals such as digital digital television (TV) broadcasting, cellular or Wireless-Fidelity (Wi-Fi) to enable batteryless devices to communicate with each other. One open problem for ambient backscatter is channel capacity. In this paper, we investigate the capacity problem of an ambient backscatter communication...
The basic energy detector still plays an important role in the extended target detection, with the advantage that it does not depend on any prior knowledge of the observed target. However, for the complex extended target model, the detector's performance has not been adequately studied. This paper will focus on the theoretical detection performance of energy detector for the fluctuating extended target...
Target detection experiments with a novel non-parametric detector are carried out exploiting the availability of a new hyperspectral data set featuring a suburban scene with several different targets. Benefiting from its non-parametric nature and from its data adaptivity deriving from the variable-bandwidth approach, the detector is shown to provide promising results for the detection of the targets...
Detection with multiple distributions is considered. Rather than formulating the problem with multiple hypotheses, we formulate the problem in a binary hypothesis testing framework by a multiple model approach. Three classes of the Multi-Model Detection (MMD) problems are considered: simplex, compound, and mixture. Three concepts of optimality are given for these three problems, including Uniformly...
The estimation of blurred regions is an important stage in several computer vision applications. In this paper an efficient training-free detector of local blurriness based on edge features is presented. Due to the intrinsic sparsity of edges in natural images a blur map is creating by using an approach based on the heat diffusion principle. A 2D point discrete Poisson solver is concatenated with...
One application of digital watermarking is multimedia copyright protection. This paper address the blind watermark detection problem that copyright protection belongs to this field. Since watermark detection process can be modeled as a statistical decision rule, selecting appropriate statistical model plays an important role. This work presents a novel additive watermark detector for contourlet domain...
Over the past few years, free-space optical (FSO) communication has gained significant attention. In fact, FSO can provide cost-effective and unlicensed links, with high-bandwidth capacity and low error rate, making it an exciting alternative to traditional wireless radio-frequency communication systems. However, the system performance is affected not only by the presence of atmospheric turbulences,...
In VLF/LF communication systems, atmospheric noise is the dominant interference, which is usually highly impulsive and can be effectively modeled as symmetric α-stable (SαS) distribution. It has been known that communication techniques designed under Gaussian noise assumption always have poor performance under impulsive noise. In this work, we focus on the detection problem of minimum shift keying...
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are...
Despite the simplicity of the energy detector, its use in cognitive radio systems is limited by the need of a priori information about the noise level. In the absence of such information, it is necessary to estimate the variance of the noise in one way or another and recalculate the threshold of detection on false alarm. If the noise is additive white Gaussian (AWGN) with zero mean, then the threshold...
In a number of scenarios, detecting the presence or absence of a known signal may be of practical interest. One such example lies in a communication setting, where packet detection is a vital first step to decode transmitted data. The detection problem can be formulated as a binary hypothesis test within the Neyman-Pearson (NP) framework. Our scenario of interest is warm shallow waters, where the...
There have been increasingly large deployments of radiation detection networks that require computationally fast algorithms to produce prompt results over ad-hoc sub-networks of mobile devices, such as smart-phones. These algorithms are in sharp contrast to complex network algorithms that necessitate all measurements to be sent to powerful central servers. In this work, at individual sensors, we employ...
We examine the adaptive detection problem in the presence of colored noise with an unknown covariance matrix, by exploiting a persymmetric structure in the received signal. The persymmetric adaptive matched filter (PS-AMF) is used to address this problem, which can significantly alleviate the requirement of secondary data. In this paper, finite-sum expressions for the probability of false alarm of...
In this paper we propose a new extraction scheme for linear arranged targets in polarimetric SAR images based on a contrario theory. In this scheme, to reduce the influence of speckle, firstly a polarimetric whitening filter is applied to combine four images from different channels into a single channel image. Then a Cell-Averaging Constant False Alarm Rate detector with Weibull clutter background...
This paper presents an automatic ship detection algorithm for polarimetric synthetic aperture radar (PolSAR) data. Based on the non-Gaussian K-Wishart distribution model for complex backscattering coefficients, the PolSAR image is clustered automatically by a modified expectation maximization algorithm. A goodness-of-fit test is incorporated to improve the model fitness of the cluster iteratively...
This report proposes a detection method based on using kurtosis and maximum amplitude information, both extracted from the spectral domain. At the end we face a classification problem which is solved by introducing a linear decision boundary. This new method is proved to result into overall good and stable detection results.
In this paper, we propose a Multi-Stage Energy Detection (ED) algorithm for spectrum sensing. Using an accurate modeling for the energies at different stages, the algorithm can minimize the average required number of samples to achieve the overall target performance, measured in terms of probabilities of detection and false alarm. Specifically, the algorithm determines the required target performances...
In this work, we extend the performance analysis of some single-pulse transmission signal processors that produce CFAR under the assumption of Pareto distributed clutter for more realistic approach where non coherent integrated pulses are transmitted. A logarithmic transformation approach, that enables true Gaussian CFAR processes to be translated for target detection in Pareto clutter scenario, is...
The mathematical model of the demodulating logarithmic amplifier has been developed. The one-dimensional probability density of a random process at the output of the logarithmic detector has been obtained. Change in the law of distribution for different values of signal-to-noise ratio at the input has been investigated.
The symmetric alpha stable distribution is a good model for impulsive interference and has gained much attention. However, most of the traditional detectors are based on the assumption that the transmission signal is known to the receivers. In non-cooperative applications such as battlefield communications, the knowledge of the intercepted signal is hardly available. How to detect a signal without...
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