Elio D. Di Claudio

Multi-Source Localization Strategies

Localization of multiple acoustic sources is an important task in many practical applications. However, in most cases adopted models are not fully adequate to describe real scenarios. In particular, in the presence of reverberation, the signal model should explicitly take into account both signals radiated by multiple sources and reflections. In alternative to Generalized Cross-Correlation methods, array processing concepts can be effectively applied to multi-source localization in reverberant environments. In this chapter, main features and limitations of wide-band array processing approaches are briefly reviewed. Furthermore, a new integrated localization and classification system, based on a robust frequency-domain Time Delay Of Arrival (TDOA) estimation followed by a spatial clustering of raw location estimates, is presented. The proposed method efficiently incorporates TDOA and array processing concepts in an unified approach. Results on simulated data are supplied.

Prefiltering approaches for time delay estimation in reverberant environments

In recent years much interest has been focused on time delay estimation in reverberant environments, for a variety of practical purposes. Algorithms based on generalized cross correlation are commonly used, but show clear limitations even in the presence of low reverberation levels. More sophisticated approaches, like cepstral prefiltering, have been proposed, but they can be computationally expensive and inadequate for real-time applications. In this paper a novel prefiltering approach, based on the common acoustical pole modeling of room transfer functions, is described and compared to existing techniques. Experimental tests show its effectiveness in combating reverberation while maintaining the simplicity requirement needed in many practical situations.

Maximum Likelihood Orientation Estimation of 1-D Patterns in Laguerre-Gauss Subspaces

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.

Complex discriminative learning Bayesian neural equalizer

Traditional approaches to channel equalization are based on the inversion of the global (linear or nonlinear) channel response. However, in digital links the complete channel inversion is neither required nor desirable. Since transmitted symbols belong to a discrete alphabet, symbol demodulation can be effectively recasted as a classification problem in the space of received symbols. In this paper a novel neural network for digital equalization is introduced and described. The proposed approach is based on a decision-feedback architecture trained with a complex-valued discriminative learning strategy for the minimization of the classification error. Main features of the resulting neural equalizer are the high rate of convergence with respect to classical neural equalizers and the low degree of complexity. Its effectiveness has been demonstrated through computer simulations for several typical digital transmission channels.

Optimal quiescent vectors for wideband ML beamforming in multipath fields

In this work, the behavior of the recently developed wideband maximum likelihood steered beamformer (ML-STBF) is analyzed under multipath conditions. In particular, the importance of an optimal choice of the quiescent vector to initialize the ML-STBF is pointed out.Some quiescent vectors, optimized on the basis of the previous analysis, are developed for the joint use with the ML-STBF and compared in simulation. It is shown that their use can significantly improve the output signal-to-noise ratio, the multipath suppression capability and the robustness with respect to focusing errors of the ML-STBF.

Wideband source localization by space-time MUSIC subspace estimation

Accurate estimation of the direction of arrivals (DOAs) of multiple wideband signal sources by sensor arrays is of paramount importance in recent developments of Ultra-Wide Band (UWB) and MIMO communication systems, acoustic applications, ultrasound, beside classical radar and sonar sensing. The array model changes with frequency. Narrowband analysis is not suited for short duration and, more in general, non-stationary sources. Most existing wideband direction finding algorithms are based on sensor output channelization (frequency binning) and neglect correlations among frequency bins, intra-bin finite bandwidth effects and spectral leakage that may create ghost sources during signal subspace estimation and impair the consistency of DOA estimators at high signal to noise (SNR) ratios. In this paper, a minimum leakage MUSIC-based estimator of subband signal subspaces from the space-time array covariance is introduced. Resulting subspace estimates can be fed to any frequency domain Maximum Likelihood (ML), Weighted Subspace Fitting (WSF) or focusing algorithm for final DOA estimation. Realistic simulations demonstrate the superior performance of the new estimator in difficult environments.

Robust direction estimation of UWB sources in Laguerre-Gauss beamspaces

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.

Total least squares approach for fast learning in multilayer neural networks

Classical methods for training feedforward neural networks are characterized by a number of shortcomings, first of all the slow rate of convergence and the occurrence of local minima. In this paper a new learning algorithm is presented as a faster alternative to the backpropagation method. The algorithm is based on the solution of a linearized system for each layer of the network performed by a block total least squares technique. Simulation results are reported showing the high convergence speed of the new algorithm and its high degree of accuracy.

Fast Classification of Dust Particles from Shadows

A fast and versatile method for classifying dust particles dispersed in the air is presented. The method uses images captured by a simple imaging system composed of a photographic sensor array and of an illuminating source. Such a device is exposed to free particulate deposition from the environment, and its accumulation is measured by observing the shadows of the particles the air casts onto the photographic sensor. Particles are detected and classified in order to measure their density and to analyse their composition. To this purpose, the contour paths of particle shadows are traced. Then, distinctive features of single particles, such as dimension and morphology, are extracted by looking at corresponding features of the sequence of local orientation changes of contours. Discrimination between dust and fibre particles is efficiently done using the varimax norm of these orientation changes. It is shown through field examples that such a technique is very well suited for quantitative and qualitative dust analysis in real environments.

Ultra wide band ISAR imaging by Laguerre Gauss tomographic reconstruction

When the angle covered by the target as seen by the sensor is sufficiently narrow, the ISAR imaging technique can be viewed as the tomographic reconstruction process of a synthetic image, which maps on space coordinates the energy backscattered from the target along different observation lines. As an alternative to the Fourier slice based techniques, in this paper a map reconstruction method from its two-dimensional (2-D) Laguerre Gauss (LG) expansion is presented. The LG expansion was already used for efficiently analyzing the orientation and the cross profile of linear patterns in images within a Maximum Likelihood approach. In particular, LG basis functions and expansion coefficients rotate by a simple phase shift, proportional to the rotation angle and the circular harmonic order. In addition, the LG expansion is linearly related to the 2-D Hermite Gauss (2-D HG) expansion, which allows for efficient numerical realizations and a compact model of the returns of Ultra Wide Band (UWB) waveforms, naturally suited for this high resolution scheme. The LG or the 2-D HG truncated expansion of the map is finally computed by the linear Least Squares interpolation of a set of matched filter outputs and used for reconstructing the backscattering map. Several system aspects of the LG ISAR method are discussed and computer experiments with tight concentrations of point scatterers illuminated by UWB pulse trains are presented.