Abstract
Algorithms of reconstruction of moving random source characteristics by processing the data obtained from near-field measurements are presented. The first algorithm, the so-called algorithm of high-frequency approximation (HFA algorithm) deduced from an asymptotic solution of an integral equation (analogy of Maue equation) allows one to construct a near-field measurement technique for transformation to far-field characteristics. The processing procedure does not depend on the source model and needs only main trajectory parameters (motion angle, initial point). The second algorithm is founded on statistic estimation of source characteristics on the basis of the maximum likelihood method [algorithm of maximum likelihood estimation with regularization (MLER algorithm)]. In contrast with the HFA algorithm, the MLER algorithm allows one to solve the inverse problem of reconstruction of the elementary source distribution on the radiator. Unlike earlier presented methods, the optimum procedure of the pseudoinversion of the matrix representing the mapping of the source into near-field samples is obtained. This procedure minimizes the reconstruction total error. Both algorithms include the compensation of external spatially correlated noise. Representative results of the extensive numerical simulations and some natural experiments for various radiators are included.
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