Near-field localization using inverse filter criteria-based blind separation and cumulant matching

This paper is concerned with the problem of near-field source localization. The problem is tackled using the method of blind separation of independent signals (sources) from their linear instantaneous (memoryless) mixtures. The various signals are assumed to be zero-mean non-Gaussian but not necessarily linear or i.i.d. Approaches using higher-order cumulants are developed using the fourth-order normalized cumulants of the “beamformed” data. The instantaneous mixture matrix is obtained by cross-correlating the extracted inputs with the observed outputs. The first approach is source-extractive, i.e., the sources are extracted and cancelled one-by-one. The other approach is based upon cumulant matching of the estimated and model-based cumulants parametrized by the source parameters (range, bearing and cumulant). Illustrative simulation examples are provided