The Experts below are selected from a list of 46545 Experts worldwide ranked by ideXlab platform
Danilo Orlando - One of the best experts on this subject based on the ideXlab platform.
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adaptive Radar Detection and classification algorithms for multiple coherent signals
IEEE Transactions on Signal Processing, 2021Co-Authors: Sudan Han, Linjie Yan, Yuxuan Zhang, Pia Addabbo, Chengpeng Hao, Danilo OrlandoAbstract:In this paper, we address the problem of target Detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers. To this end, we formulate the problem at hand as a multiple-hypothesis test that, besides the conventional Radar alternative hypothesis, contains additional hypotheses accounting for the presence of an unknown number of interfering signals. In this context and leveraging the classification capabilities of the Model Order Selection rules, we devise penalized likelihood-ratio-based Detection architectures that can establish, as a byproduct, which hypothesis is in force. Moreover, we propose a suboptimum procedure to estimate the angles of arrival of multiple coherent signals ensuring (at least for the considered parameters) almost the same performance as the exhaustive search. Finally, the performance assessment, conducted over simulated data and in comparison with conventional Radar detectors, highlights that the proposed architectures can provide satisfactory performance in terms of probability of Detection and correct classification.
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Radar Detection architecture based on interference covariance structure classification
IEEE Transactions on Aerospace and Electronic Systems, 2019Co-Authors: Vincenzo Carotenuto, Antonio De Maio, Danilo Orlando, Petre StoicaAbstract:This paper proposes a new Radar Detection architecture, which is composed of an interference covariance structure classifier and a bank of adaptive Radar detectors. The classifier is based on model order selection theory and the Bayesian information criterion to determine the covariance structure that is deemed to be suitable for a specific set of Radar data [1] . This decision stage drives the choice of the Radar detector within a specific class of adaptive detectors to establish the possible target presence. The critical issue concerning the constant false alarm rate behavior of the architecture is discussed and two techniques for the threshold setting process are proposed. Finally, the Detection performance analysis, conducted on both simulated and measured data, shows that the proposed architecture can guarantee better performance than classic Radar decision schemes in scenarios where the interference covariance exhibits structural properties.
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adaptive Radar Detection of a subspace signal embedded in subspace structured plus gaussian interference via invariance
IEEE Transactions on Signal Processing, 2016Co-Authors: Antonio De Maio, Danilo OrlandoAbstract:This paper deals with adaptive Radar Detection of a subspace signal competing with two sources of interference. The former is Gaussian with unknown covariance matrix and accounts for the joint presence of clutter plus thermal noise. The latter is structured as a subspace signal and models coherent pulsed jammers impinging on the Radar antenna. The problem is solved via the Principle of Invariance which is based on the identification of a suitable group of transformations leaving the considered hypothesis testing problem invariant. A maximal invariant statistic, which completely characterizes the class of invariant decision rules and significantly compresses the original data domain, as well as its statistical characterization are determined. Thus, the existence of the optimum invariant detector is addressed together with the design of practically implementable invariant decision rules. At the analysis stage, the performance of some receivers belonging to the new invariant class is established through the use of analytic expressions.
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adaptive Radar Detection in the presence of gaussian clutter with symmetric spectrum
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Chengpeng Hao, Antonio De Maio, Danilo Orlando, Salvatore Iommelli, Chaohuan HouAbstract:In this paper, we address the problem of detecting the signal of interest in the presence of Gaussian clutter with symmetric spectrum. To this end, we exploit the spectral properties of the clutter to transfer the binary hypothesis test problem from complex domain to real domain. Then, we devise and assess a Detection strategy based on the so-called two-step Generalized Likelihood Ratio Test (GLRT) design procedure. Finally, a preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detector compared with the traditional state-of-the-art counterparts which ignore the spectrum symmetry.
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adaptive Radar Detection and localization of a point like target
IEEE Transactions on Signal Processing, 2011Co-Authors: Danilo Orlando, G RicciAbstract:In the present paper, we focus on the design of adaptive decision schemes for point-like targets; the proposed algorithms can take advantage of the possible spillover of target energy between consecutive matched filter samples. To this end, we assume that the received useful signal is known up to a complex factor modeled as a deterministic parameter; moreover, it is embedded in correlated Gaussian noise with unknown covariance matrix. Finally, for estimation purposes we assume that a set of secondary data, free of signal components, but sharing the same covariance matrix of the noise in the cells containing signal returns, up to a possibly different scale factor, is available. Remarkably, the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure the desirable constant false alarm rate property with respect to the unknown noise parameters.
Michael R Souryal - One of the best experts on this subject based on the ideXlab platform.
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deep learning classification of 3 5 ghz band spectrograms with applications to spectrum sensing
IEEE Transactions on Cognitive Communications and Networking, 2019Co-Authors: Max W Lees, Adam Wunderlich, Peter Jeavons, Paul D Hale, Michael R SouryalAbstract:In the United States, the Federal Communications Commission has adopted rules permitting commercial wireless networks to share spectrum with federal incumbents in the 3.5-GHz Citizens Broadband Radio Service band. These rules require commercial systems to vacate the band when sensors detect Radars operated by the U.S. military; a key example being the SPN-43 air traffic control Radar. Such sensors require highly accurate Detection algorithms to meet their operating requirements. In this paper, using a library of over 14 000 3.5-GHz band spectrograms collected by a recent measurement campaign, we investigate the performance of 13 methods for SPN-43 Radar Detection. Namely, we compare classical methods from signal Detection theory and machine learning to several deep learning architectures. We demonstrate that machine learning algorithms appreciably outperform classical signal Detection methods. Specifically, we find that a three-layer convolutional neural network offers a superior tradeoff between accuracy and computational complexity. Last, we apply this three-layer network to generate descriptive statistics for the full 3.5-GHz spectrogram library. Our findings highlight potential weaknesses of classical methods and strengths of modern machine learning algorithms for Radar Detection in the 3.5-GHz band.
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deep learning classification of 3 5 ghz band spectrograms with applications to spectrum sensing
arXiv: Signal Processing, 2018Co-Authors: Max W Lees, Adam Wunderlich, Peter Jeavons, Paul D Hale, Michael R SouryalAbstract:In the United States, the Federal Communications Commission has adopted rules permitting commercial wireless networks to share spectrum with federal incumbents in the 3.5~GHz Citizens Broadband Radio Service band. These rules require commercial systems to vacate the band when sensors detect Radars operated by the U.S. military; a key example being the SPN-43 air traffic control Radar. Such sensors require highly-accurate Detection algorithms to meet their operating requirements. In this paper, using a library of over 14,000 3.5~GHz band spectrograms collected by a recent measurement campaign, we investigate the performance of thirteen methods for SPN-43 Radar Detection. Namely, we compare classical methods from signal Detection theory and machine learning to several deep learning architectures. We demonstrate that machine learning algorithms appreciably outperform classical signal Detection methods. Specifically, we find that a three-layer convolutional neural network offers a superior tradeoff between accuracy and computational complexity. Last, we apply this three-layer network to generate descriptive statistics for the full 3.5~GHz spectrogram library. Our findings highlight potential weaknesses of classical methods and strengths of modern machine learning algorithms for Radar Detection in the 3.5~GHz band.
G Ricci - One of the best experts on this subject based on the ideXlab platform.
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adaptive Radar Detection and localization of a point like target
IEEE Transactions on Signal Processing, 2011Co-Authors: Danilo Orlando, G RicciAbstract:In the present paper, we focus on the design of adaptive decision schemes for point-like targets; the proposed algorithms can take advantage of the possible spillover of target energy between consecutive matched filter samples. To this end, we assume that the received useful signal is known up to a complex factor modeled as a deterministic parameter; moreover, it is embedded in correlated Gaussian noise with unknown covariance matrix. Finally, for estimation purposes we assume that a set of secondary data, free of signal components, but sharing the same covariance matrix of the noise in the cells containing signal returns, up to a possibly different scale factor, is available. Remarkably, the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure the desirable constant false alarm rate property with respect to the unknown noise parameters.
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advanced Radar Detection schemes under mismatched signal models
Synthesis Lectures on Signal Processing, 2009Co-Authors: Danilo Orlando, Francesco Bandiera, G RicciAbstract:Adaptive Detection of signals embedded in correlated Gaussian noise has been an active field of research in the last decades. This topic is important in many areas of signal processing such as, just to give some examples, Radar, sonar, communications, and hyperspectral imaging. Most of the existing adaptive algorithms have been designed following the lead of the derivation of Kelly's detector which assumes perfect knowledge of the target steering vector. However, in realistic scenarios, mismatches are likely to occur due to both environmental and instrumental factors. When a mismatched signal is present in the data under test, conventional algorithms may suffer severe performance degradation. The presence of strong interferers in the cell under test makes the Detection task even more challenging. An effective way to cope with this scenario relies on the use of "tunable" detectors, i.e., detectors capable of changing their directivity through the tuning of proper parameters. The aim of this book is to present some recent advances in the design of tunable detectors and the focus is on the so-called two-stage detectors, i.e., adaptive algorithms obtained cascading two detectors with opposite behaviors. We derive exact closed-form expressions for the resulting probability of false alarm and the probability of Detection for both matched and mismatched signals embedded in homogeneous Gaussian noise. It turns out that such solutions guarantee a wide operational range in terms of tunability while retaining, at the same time, an overall performance in presence of matched signals commensurate with Kelly's detector. Table of Contents: Introduction / Adaptive Radar Detection of Targets / Adaptive Detection Schemes for Mismatched Signals / Enhanced Adaptive Sidelobe Blanking Algorithms / Conclusions
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adaptive Radar Detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference
IEEE Transactions on Signal Processing, 2007Co-Authors: Francesco Bandiera, A.s.d. Greco, Antonio De Maio, G RicciAbstract:This paper addresses adaptive Radar Detection of distributed targets in noise plus interference assumed to belong to a known or unknown subspace of the observables. At the design stage we resort to either the GLRT or the so-called two-step GLRT-based design procedure and assume that a set of noise-only data is available (the so-called secondary data). Detection algorithms have been derived modeling noise vectors, corresponding to different range cells, as independent, zero-mean, complex normal ones, sharing either the same covariance matrix (homogeneous environment) or the same covariance matrix up to possibly different (mean) power levels between primary data, i.e., range cells under test, and secondary ones (partially homogeneous environment). The performance assessment has been conducted by Monte Carlo simulation, also in comparison to previously proposed Detection algorithms, and confirms the effectiveness of the newly proposed ones
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adaptive Radar Detection of extended targets via signature diversity
Asilomar Conference on Signals Systems and Computers, 2002Co-Authors: Francesco Bandiera, G Ricci, M TesauroAbstract:The paper addresses adaptive Detection of extended targets in Gaussian noise with unknown statistics. It is assumed that the Radar can change the transmitted signal in azimuth. More precisely, it makes use of N N-dimensional signatures; the possible useful signal from each Radar cell is a coherent pulse train while the disturbance is a Gaussian process, independent from cell to cell, but with the same (unknown) covariance matrix regardless of the illuminated one. Based on the above model, we propose an adaptive detector designed according to a two-step procedure. Its performance assessment and the comparison with a previously-proposed detector show that the proposed one can be a viable means to cope with uncertain scenarios.
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adaptive matched filter Detection in spherically invariant noise
IEEE Signal Processing Letters, 1996Co-Authors: E Conte, M Lops, G RicciAbstract:The article addresses Radar Detection of coherent pulse trains embedded in spherically invariant noise with unknown statistics. Starting upon a newly proposed detector, which assumes knowledge of the structure of the clutter covariance matrix, we substitute the actual matrix by a proper estimate based on a set of secondary data vectors. Interestingly, the resulting detector achieves a constant false alarm rate with respect to the texture component of the clutter, and incurs an acceptable loss with respect to the case of a known covariance matrix.
Max W Lees - One of the best experts on this subject based on the ideXlab platform.
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deep learning classification of 3 5 ghz band spectrograms with applications to spectrum sensing
IEEE Transactions on Cognitive Communications and Networking, 2019Co-Authors: Max W Lees, Adam Wunderlich, Peter Jeavons, Paul D Hale, Michael R SouryalAbstract:In the United States, the Federal Communications Commission has adopted rules permitting commercial wireless networks to share spectrum with federal incumbents in the 3.5-GHz Citizens Broadband Radio Service band. These rules require commercial systems to vacate the band when sensors detect Radars operated by the U.S. military; a key example being the SPN-43 air traffic control Radar. Such sensors require highly accurate Detection algorithms to meet their operating requirements. In this paper, using a library of over 14 000 3.5-GHz band spectrograms collected by a recent measurement campaign, we investigate the performance of 13 methods for SPN-43 Radar Detection. Namely, we compare classical methods from signal Detection theory and machine learning to several deep learning architectures. We demonstrate that machine learning algorithms appreciably outperform classical signal Detection methods. Specifically, we find that a three-layer convolutional neural network offers a superior tradeoff between accuracy and computational complexity. Last, we apply this three-layer network to generate descriptive statistics for the full 3.5-GHz spectrogram library. Our findings highlight potential weaknesses of classical methods and strengths of modern machine learning algorithms for Radar Detection in the 3.5-GHz band.
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deep learning classification of 3 5 ghz band spectrograms with applications to spectrum sensing
arXiv: Signal Processing, 2018Co-Authors: Max W Lees, Adam Wunderlich, Peter Jeavons, Paul D Hale, Michael R SouryalAbstract:In the United States, the Federal Communications Commission has adopted rules permitting commercial wireless networks to share spectrum with federal incumbents in the 3.5~GHz Citizens Broadband Radio Service band. These rules require commercial systems to vacate the band when sensors detect Radars operated by the U.S. military; a key example being the SPN-43 air traffic control Radar. Such sensors require highly-accurate Detection algorithms to meet their operating requirements. In this paper, using a library of over 14,000 3.5~GHz band spectrograms collected by a recent measurement campaign, we investigate the performance of thirteen methods for SPN-43 Radar Detection. Namely, we compare classical methods from signal Detection theory and machine learning to several deep learning architectures. We demonstrate that machine learning algorithms appreciably outperform classical signal Detection methods. Specifically, we find that a three-layer convolutional neural network offers a superior tradeoff between accuracy and computational complexity. Last, we apply this three-layer network to generate descriptive statistics for the full 3.5~GHz spectrogram library. Our findings highlight potential weaknesses of classical methods and strengths of modern machine learning algorithms for Radar Detection in the 3.5~GHz band.
Krijn De Vries - One of the best experts on this subject based on the ideXlab platform.
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probing the Radar scattering cross section for high energy particle cascades in ice
Proceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017), 2017Co-Authors: Krijn De Vries, R Abbasi, J W Belz, D Besson, Krijn D De Vries, M A Duvernois, K Hanson, D Ikeda, U Latif, J N MatthewsAbstract:Recently the Radar scattering technique to probe neutrino induced particle cascades above PeV energies in ice was investigated. The feasibility of the Radar Detection method was shown to crucially depend on several up to now unknown plasma properties, such as the plasma lifetime and the free charge collision rate. To determine these parameters, a Radar scattering experiment was performed at the Telescope Array Electron Light Source facility, where a beam of high-energy electrons was directed in a block of ice. The induced ionization plasma was consequently probed using a Radar Detection set-up detecting over a wide frequency range from 200 MHz up to 2 GHz. First qualitative results of this experiment will be presented.
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modeling the Radar scatter off of high energy neutrino induced particle cascades in ice
European Physical Journal Web of Conferences, 2017Co-Authors: Krijn De Vries, Nick Van Eijndhoven, O Scholten, S Toscano, A OmurchadhaAbstract:We discuss the Radar Detection method as a probe for high-energy neutrino induced particle cascades in ice. In a previous work we showed that the Radar Detection techniqe is a promising method to probe the high-energy cosmic neutrino flux above PeV energies. This was done by considering a simplified cascade geometry and scattering model. In this article we discuss the scattering in more detail. We provide a model for the Radar cross-section based on the induced plasma properties, and discuss the angular dependence of the scatter.
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on the feasibility of Radar Detection of high energy cosmic neutrinos
Proceedings of The 34th International Cosmic Ray Conference — PoS(ICRC2015), 2016Co-Authors: Krijn De Vries, K Hanson, T Meures, A OmurchadhaAbstract:We discuss the Radar Detection technique as a probe for high-energy cosmic neutrino induced particle cascades in a dense medium like ice. With the recent Detection of high-energy cosmic neutrinos by the IceCube neutrino observatory the window to neutrino astronomy has been opened. We discuss a new technique to detect cosmic neutrinos at even higher energies than those covered by IceCube, but with an energy threshold below the currently operating Askaryan radio detectors. A calculation for the Radar return power, as well as first experimental results will be presented.
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on the feasibility of Radar Detection of high energy neutrino induced showers in ice
Astroparticle Physics, 2015Co-Authors: Krijn De Vries, K Hanson, T MeuresAbstract:Abstract In this article we try to answer the question whether the Radar Detection technique can be used for the Detection of high-energy-neutrino induced particle cascades in ice. A high-energy neutrino interacting in ice will induce a particle cascade, also referred to as a particle shower, moving at approximately the speed of light. Passing through, the cascade will ionize the medium, leaving behind a plasma tube. The different properties of the plasma-tube, such as its lifetime, size and the charge-density will be used to obtain an estimate if it is possible to detect this tube by means of the Radar Detection technique. Next to the ionization electrons a second plasma due to mobile protons induced by the particle cascade is discussed. An energy threshold for the cascade inducing particle of 4 PeV for the electron plasma, and 20 PeV for the proton plasma is obtained. This allows the Radar Detection technique, if successful, to cover the energy-gap between several PeV and a few EeV in the currently operating neutrino detectors, where on the low side IceCube runs out of events, and on the high side the Askaryan radio detectors begin to have large effective volumes.
