Bernard Picinbono

Bio: Bernard Picinbono is an academic researcher from University of Paris-Sud. The author has contributed to research in topics: White noise & Linear filter. The author has an hindex of 17, co-authored 66 publications receiving 2249 citations. Previous affiliations of Bernard Picinbono include Supélec & Centre national de la recherche scientifique.

Widely linear estimation with complex data

Abstract: Mean square estimation of complex and normal data is not linear as in the real case but widely linear. The purpose of the paper is to calculate the optimum widely linear mean square estimate and to present its main properties. The advantage with respect to the linear procedure is especially analyzed. >

On instantaneous amplitude and phase of signals

Abstract: In many questions of signal processing, it is important to use the concepts of instantaneous amplitude or phase of signals. This is especially the case in communication systems with amplitude or frequency modulation. These concepts are often introduced empirically. However, it is well known that the correct approach for this purpose is to use the concept of analytic signal. Starting from this point, we show some examples of contradictions appearing when using other definitions of instantaneous amplitude or frequency that are commonly admitted. This introduces the problem of characterizing pure amplitude-modulated or pure phase-modulated signals. It is especially shown that whereas amplitude modulated signals can be characterized by spectral considerations, this is no longer the case for phase-modulated signals. Furthermore, signals with constant amplitude have very specific properties, which are analyzed in detail. Some consequences and extensions to random signals are finally discussed.

Second-order complex random vectors and normal distributions

Abstract: Complex random vectors are usually described by their covariance matrix. This is insufficient for a complete description of second-order statistics, and another matrix called the relation matrix is necessary. Some of its properties are analyzed and used to express the probability density function of normal complex vectors. Various consequences are presented.

Second-order statistics of complex signals

Abstract: The second-order statistical properties of complex signals are usually characterized by the covariance function. However, this is not sufficient for a complete second-order description, and it is necessary to introduce another moment called the relation function. Its properties, and especially the conditions that it must satisfy, are analyzed both for stationary and nonstationary signals. This leads to a new perspective concerning the concept of complex white noise as well as the modeling of any signal as the output of a linear system driven by a white noise. Finally, this is applied to complex autoregressive signals, and it is shown that the classical prediction problem must be reformulated when the relation function is taken into consideration.

On deflection as a performance criterion in detection

Abstract: The performance of detection systems is usually characterized by the receiver operating characteristics (ROC) curves. Because of the complexity of the calculation of such curves simpler performance criteria are useful. Among these, the deflection criterion, or output signal-to-noise ratio, is one of the most interesting. The advantages and limitations of the deflection are first analyzed. Attention is especially paid to the relation between singular detection and infinite deflection. Maximization of the deflection in the class of linear-quadratic (L-Q) systems is also discussed and it is shown that in order to detect a deterministic signal in some kinds of non-gaussian noise a quadratic term can introduce an almost singular detection. This situation is analyzed by computer simulations and comparisons between results obtained via the deflection or the ROC curves indicate the interest of the deflection in the analysis of the performance of some nonlinear system. >

An Introduction To The Theory Of Point Processes

Abstract: Thank you for downloading an introduction to the theory of point processes. As you may know, people have search hundreds times for their chosen novels like this an introduction to the theory of point processes, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their computer. an introduction to the theory of point processes is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the an introduction to the theory of point processes is universally compatible with any devices to read.

Spectrum Sensing for Cognitive Radio

Abstract: Spectrum sensing is the very task upon which the entire operation of cognitive radio rests. For cognitive radio to fulfill the potential it offers to solve the spectrum underutilization problem and do so in a reliable and computationally feasible manner, we require a spectrum sensor that detects spectrum holes (i.e., underutilized subbands of the radio spectrum), provides high spectral-resolution capability, estimates the average power in each subband of the spectrum, and identifies the unknown directions of interfering signals. Cyclostationarity is another desirable property that could be used for signal detection and classification. The multitaper method (MTM) for nonparametric spectral estimation accomplishes these tasks accurately, effectively, robustly, and in a computationally feasible manner. The objectives of this paper are to present: 1) tutorial exposition of the MTM, which is expandable to perform space-time processing and time-frequency analysis; 2) cyclostationarity, viewed from the Loeve and Fourier perspectives; and 3) experimental results, using Advanced Television Systems Committee digital television and generic land mobile radio signals, followed by a discussion of the effects of Rayleigh fading.

Adaptive subspace detectors

Abstract: We use the theory of generalized likelihood ratio tests (GLRTs) to adapt the matched subspace detectors (MSDs) of Scharf (1991) and of Scharf and Frielander (1994) to unknown noise covariance matrices. In so doing, we produce adaptive MSDs that may be applied to signal detection for radar, sonar, and data communication. We call the resulting detectors adaptive subspace detectors (ASDs). These include Kelly's (1987) GLRT and the adaptive cosine estimator (ACE) of Kaurt and Scharh (see ibid., vol.47, p.2538-41, 1999) and of Scharf and McWhorter (see Proc. 30th Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, 1996) for scenarios in which the scaling of the test data may deviate from that of the training data. We then present a unified analysis of the statistical behavior of the entire class of ASDs, obtaining statistically identical decompositions in which each ASD is simply decomposed into the nonadaptive matched filter, the nonadaptive cosine or t-statistic, and three other statistically independent random variables that account for the performance-degrading effects of limited training data.