1,721,037 research outputs found
Radar detection of fluctuating targets in Compound-Gaussian clutter
No full textThe paper deals with the detection of fluctuating targets in the presence of Compound-Gaussian clutter. We show that the optimum receiver, according to the Neyman-Pearson criterion, is canonical in that its structure is independent of the clutter amplitude probability density function. Moreover it is interpretable as a conventional square law receiver with an adaptive threshold. The performance analysis shows that the proposed detector largely outperforms the conventional receiver. Unfortunately the Neyman-Pearson receiver is hardly implementable since it requires the knowledge of the target strength. Nevertheless not only it provides guidance to the design of suboptimal realizable detectors, but also its performance is an upper bound for those achievable by any other detector
CFAR detection of multidimensional signals: An invariant approach
No full textThe paper deals with constant false alarm rate (CFAR) detection of multidimensional signals embedded in Gaussian noise with unknown covariance. We attack the problem by resorting to the principle of invariance,which proves a valuable statistical tool for ensuring a priori, namely at the design stage, the CFAR property. In this context, we determine a maximal invariant statistic with respect to a proper group of transformations that leave unaltered the hypothesis-testing problem under study, devise the optimum invariant detector, and show that no uniformly most powerful invariant (UMPI) test exists. Thus, we establish the conditions an invariant detector must fulfill in order to ensure the CFAR property. Finally, we discuss several suboptimal (implementable) invariant receivers and, remarkably, show that the generalized likelihood ratio test (GLRT) detector is a member of this class. The performance analysis, which has been carried out in the presence of a Gaussian signal array, shows that the proposed detectors exhibit a quite acceptable loss with respect to the optimum Neyman-Pearson detector
Statistical analysis of real clutter at different range resolutions
No full textA statistical analysis is presented of real radar clutter data collected using the McMaster IPIX radar in 1998 and stored in the Grimsby database. We first show the deviations of the amplitude statistics from the Rayleigh model and the suitability of the K- and Weibull-distribution for the first-order amplitude statistical characterization. Thus we focus on the I and Q components of the available data and study their statistical compatibility with the compound Gaussian model. Towards. this goal it has been necessary devising appropriate testing procedures; in particular, with reference to the higher order. statistics agreement, we have designed a validation procedure involving the clutter representation into generalized spherical coordinates. Remarkably the results have confirmed the suitability of the spherically invariant random processes (SIRPs) for the correct modeling of the radar clutter. Finally we have performed a spectral analysis highlighting the close matching between the estimated clutter spectral density and the exponential model
Signal detection in compound-Gaussian noise: Neyman-Pearson and CFAR detectors
No full textThis paper handles the problem of detecting signals with known signature and unknown or random amplitude and phase in the presence of compound-Gaussian disturbance with known spectral density. Two alternative approaches are investigated: the Neyman-Pearson criterion and the generalized likelihood ratio strategy. The first approach leads to a hardly implementable detector but provides an upper bound for the performance of any other detector. The generalized likelihood ratio strategy, instead, leads to a canonical detector, whose structure is independent of the disturbance amplitude probability density function. Based on this result, the threshold setting, which is itself independent on both the noise distribution and the signal parameters, ensures a constant false alarm rate. Unluckily, this receiver requires the averaging of infinitely many components of the received waveform. This is not really a drawback since a close approximation can be found for a practical implementation of the receiver. The performance analysis shows that the generalized likelihood ratio test (GLRT) detector suffers a quite small loss with respect to the optimum Neyman-Pearson receiver (less than 1 dB in the case of random amplitude) and largely outperforms the conventional square-law detector
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Station Placement in Networks
No full textIn this paper we study the Station Placement problem on directed graphs, a problem that has applications to efficient multicasting in circuit-switched networks. We first argue that the problem on general directed graphs can be efficiently reduced to computing bounded depth Steiner tree on complete weighted directed graphs. Then, we concentrate on the case in which the graph is a directed tree and we give polynomial time algorithms to solve the problem and a natural variant of the problem
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