Localization of Far-Field Sources with an Array of Unknown Geometry

Abstract

In recent years, an extensive body of theory and practice has been developed for spatial array processing. The need of higher resolution has led to an evolution in these techniques, from the classical beamforming to the CAPON estimator [1], autoregressive methods [2], and finally to signal subspace methods [3,4,5]. All these techniques assume that the shape of the array is exactly known. The method that we present here extends the eigenvector methods to an array of unknown geometry (a field of randomly distributed sensors) or to an array whose sensor position are approximatively known (a large towed array). The number N of sensors is assumed to be large compared to the number P of sources. The transmitted signals are assumed to be stationary, broadband processes; further it is assumed that the signals received on the array are scaled versions of the signals transmitted by the sources, without any differential attenuation between two sensors. The signals and the noise are uncorrelated. The noise itself is assumed to be spatially white. The proposed method is divided in two parts: the multifrequency analysis and the localization.