Detection of Gaussian Signal Using Adaptively Whitened Data

The adaptive matched filter, like many other adaptive detection schemes, uses in its test statistic the data under test whitened by the sample covariance matrix S of the training samples. Actually, it is a generalized likelihood ratio test (GLRT) based on the conditional (i.e., for given S) distribution of the adaptively whitened data. In this letter, we investigate detection of a Gaussian rank-one signal using the marginal (unconditional) distribution of the adaptively whitened data. A first contribution is to derive the latter and to show that it only depends on a scalar parameter, namely the signal to noise ratio. Then, a GLRT is formulated from this unconditional distribution and shown to have the constant false alarm rate property. We show that it bears close resemblance with the plain GLRT based on the whole data set (data under test and training samples). The new detector performs as well as the plain GLRT and even better with multiple cells under test and low training sample support

Adaptive spectral identification techniques in presence of undetected non linearities

The standard procedure for detection of gravitational wave coalescing binaries signals is based on Wiener filtering with an appropriate bank of template filters. This is the optimal procedure in the hypothesis of addictive Gaussian and stationary noise. We study the possibility of improving the detection efficiency with a class of adaptive spectral identification techniques, analyzing their effect in presence of non stationarities and undetected non linearities in the noiseComment: 4 pages, 2 figures, uses ws-procs9x6.cls Proceedings of "Non linear physics: theory and experiment. II", Gallipoli (Lecce), 200

Adaptive Detection Using Whitened Data When Some of the Training Samples Undergo Covariance Mismatch

We consider adaptive detection of a signal of interest when two sets of training samples are available, one sharing the same covariance matrix as the data under test, the other set being mismatched. The approach proposed in this letter is to whiten both the data under test and the matched training samples using the sample covariance matrix of the mismatched training samples. The distribution of the whitened data is then derived and subsequently the generalized likelihood ratio test is obtained. Numerical simulations show that it performs well and is rather robust

Adaptive subspace detectors

Includes bibliographical references.In this paper, we use the theory of generalized likelihood ratio tests (GLRTs) to adapt the matched subspace detectors (MSDs) of [1] and [2] 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 GLRT and the adaptive cosine estimator (ACE) of [6] and [19] 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.This work was supported by the Office of Naval Research under Contracts N00014-89-J-1070 and N00014-00-1-0033, and by the National Science Foundation under Contracts MIP-9529050 and ECS 9979400

A control algorithm for autonomous optimization of extracellular recordings

This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed

Of `Cocktail Parties' and Exoplanets

The characterisation of ever smaller and fainter extrasolar planets requires an intricate understanding of one's data and the analysis techniques used. Correcting the raw data at the 10^-4 level of accuracy in flux is one of the central challenges. This can be difficult for instruments that do not feature a calibration plan for such high precision measurements. Here, it is not always obvious how to de-correlate the data using auxiliary information of the instrument and it becomes paramount to know how well one can disentangle instrument systematics from one's data, given nothing but the data itself. We propose a non-parametric machine learning algorithm, based on the concept of independent component analysis, to de-convolve the systematic noise and all non-Gaussian signals from the desired astrophysical signal. Such a `blind' signal de-mixing is commonly known as the `Cocktail Party problem' in signal-processing. Given multiple simultaneous observations of the same exoplanetary eclipse, as in the case of spectrophotometry, we show that we can often disentangle systematic noise from the original light curve signal without the use of any complementary information of the instrument. In this paper, we explore these signal extraction techniques using simulated data and two data sets observed with the Hubble-NICMOS instrument. Another important application is the de-correlation of the exoplanetary signal from time-correlated stellar variability. Using data obtained by the Kepler mission we show that the desired signal can be de-convolved from the stellar noise using a single time series spanning several eclipse events. Such non-parametric techniques can provide important confirmations of the existent parametric corrections reported in the literature, and their associated results. Additionally they can substantially improve the precision exoplanetary light curve analysis in the future.Comment: ApJ accepte

Géométries, invariances et interprétations par le rapport signal à bruit de détecteurs en sous-espaces adaptés ou adaptatifs

Matched subspace detectors generalize the matched filter by accommodating signals that are only constrained to lie in a multidimensional subspace. There are four of these detectors, depending upon knowledge of signal phase and noise power. The adaptive subspace detectors generalize the matched subspace detectors by accommodating problems where the noise covariance matrix is unknow, and must be estimated from training data. In this paper we review the geometries and invariances of the matched and adaptive subspace detectors. We also establish that every version of a matched or adaptative subspace detectors can be interpreted as an estimator of output signal-to-noise ratio (SNR), in disquise