Long baseline interferometry now faces two main challenges. The first one is the image reconstruction from interferometric data. Indeed, the reduced information on the phase of the object Fourier transform during an observation makes image reconstruction quite critical. The second challenge is the improvements of the
overall sensitivity. Strong improvement are expected from double field interferometry : For instance, double field
interferometry enables phase referencing which is a way to obtain information on phases. In addition, double
field interferometry increases the sensitivity of an interferometer.
In this paper we present simulations of double field interferometry. Our simulations take into account; the turbulence conditions, the detection noise, the fringe tracking error. For simulated double field data, we perform an image reconstruction using the coaddition of fringes in the image plane. Since the performances of a double field interferometer are very closely related to and dependent on the site characteristics we studied the results for two different locations: Paranal and Dome C. The comparison shows that Dome C offers much better results, and that it is probably the best site on Earth to build a double-field interferometer.
KEYWORDS: Probability theory, Binary data, Sensors, Detection theory, Information theory, Stochastic processes, Signal processing, Signal detection, Neurons, Measurement devices
In this paper we revisit the asymmetric binary channel from the double point of view of detection theory and information theory. We first evaluate the capacity of the asymmetric binary channel as a function of the probabilities of false alarm and of detection, thus allowing a noise distribution independent analysis. This sets the a priori probabilities of the hypotheses and couples the two points of view. We then study the simple realization of the asymmetric binary channel using a threshold device. We particularly revisit noise-enhanced processing for subthreshold signals using the aforementioned parametrization of the capacity, and we report a somewhat paradoxical effect: using the channel at its capacity precludes in general an optimal detection.
In this paper we treat the problem of robust entropy estimation given a multidimensional random sample from an unknown distribution. In particular, we consider estimation of the Renyi entropy of fractional order which is insensitive to outliers, e.g. high variance contaminating distributions, using the k-point minimal spanning tree. A greedy algorithm for approximating the NP-hard problem of computing the k-minimal spanning tree is given which is a generalization of the potential function partitioning method of Ravi et al. The basis for our approach is asymptotic theorem establishing that the log of the overall length or weight of the greedy approximation is a strongly consistent estimator of the Renyi entropy. Quantitative robustness of the estimator to outliers is established using Hampel's method of influence functions. The structure of the influence function indicates that the k-MST is a natural extension of the 1D, (alpha) -trimmed mean for multi- dimensional data.
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