A novel method for estimating the direction of arrival of ultra-wideband wavefronts impinging on a linear uniform sensor array is proposed. It is based on expanding the space time signals onto a basis of Laguerre-Gauss Circular Harmonic functions, whose peculiar properties lead to a new signal subspace parametric model. This approach allows robust angle of arrival estimation in low signal to noise ratio environments.

The nice relationship existing among the Hermite-Gauss and the Laguerre-Gauss image expansions, whose basis functions span the same signal space, is investigated. As a result, a novel efficient method for Cartesian to polar coordinate inter-conversion, especially suited for dedicated hardware realization, is proposed. Applications to local image rotation, based on the simple steerability of the Laguerre-Gauss expansion, and to local image analysis, based on Gaussian derivatives, are considered in detail. A possible fixed-point realization of the proposed inter-conversion scheme is discussed.

A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with Laguerre-Gauss circular harmonic (LG-CH) functions, is presented. It lies on the property that the polar separable LG-CH functions span the same space as the 2-D Cartesian separable Hermite-Gauss (2-D HG) functions. Exploiting the simple steerability of the LG-CH functions and the peculiar block-linear relationship among the two expansion coefficients sets, maximum likelihood (ML) estimates of orientation and cross section parameters of 1-D patterns are obtained projecting them in a proper subspace of the 2-D HG family. It is shown in this paper that the conditional ML solution, derived by elimination of the cross section parameters, surprisingly yields the same asymptotic accuracy as the ML solution for known cross section parameters. The accuracy of the conditional ML estimator is compared to the one of state of art solutions on a theoretical basis and via simulation trials. A thorough proof of the key relationship between the LG-CH and the 2-D HG expansions is also provided.

Advances in digital imaging technologies make increasingly available high quality images characterized by high dynamic range (HDR) of color components. Usual display devices are unable to visualize the information content of HDR images, especially in presence of light. For this reason special compression techniques for image rendering on conventional displays have been developed. In this work, a new technique based on simultaneous compression of low frequency radiance variations and detail enhancement is presented. In this work, a method working in the transform domain of an edge oriented wavelet transform is presented. Low-frequency components and high-resolution wavelet coefficients are separately manipulated before image reconstruction in order to reduce the overall dynamic range. The effectiveness of the technique is shown by means of significant examples.

The convergence behaviour of the constant modulus algorithm (CMA) is analytically evaluated in terms of the mean and the variance of the equalized sequence conditioned on the transmitted symbol, thus characterizing the dispersion pattern of the equalized samples around the QAM constellation points. This extends to the CMA and improves the approach of Weerackody et al. (see ibid., vol.40, no.6, p.1383, 1992) , where the MSE trajectory of the generalized Sato algorithm (GSA) was analysed.>

The polarity coincidence estimator of the spectral centroid counts the net number of times the phasor of a stationary Gaussian process crosses a chosen phase-reference straight line through the origin during a time T. By following Pawula's (1968) approach, the covariance of two different simultaneous polarity-coincidence estimates having distinct phase reference lines is derived. This basic result is applied to compute the variance of a generalized polarity-coincidence estimator considering N arbitrarily chosen reference lines.>

The complementary aspects of spectrum estimation based on higher-order order statistics are considered. Higher-order analysis of stochastic array data is formalized in a general fashion through the introduction of the class of hybrid nonlinear (HNL) moments. HNL moments comprise, as a special case, slice cumulants and their mixtures. In this framework, the case of joint use of covariance and fourth-order slice cumulant is handled in a simple and direct way. Simulation results confirm the theoretical analysis.>