Array Processing for Detection and Localization of Narrowband, Wideband and Distributed Sources

The detection and estimation techniques that are used in array processing depend on the spatial and temporal characteristics of the signals that arrive at the array. In this dissertation, we consider narrowband as well as wideband signals. For narrowband signals, a detection method based on the predictive stochastic complexity (PSC) is developed. The PSC of data is computed for all the models with order smaller than the number of sensors. The number of signals is selected by choosing the minimum of the PSC over all models. The PSC criterion is on-line and can be used for time varying systems and target tracking. We also consider wideband signals. One approach to wideband array processing is based on sampling the spectrum of the source signals to generate narrowband signals. Then, using a focusing approach, the information at di erent frequency bins are combined. Here, an optimal method to select a focusing subspace for the well-known coherent signal-subspace method (CSM) is proposed. It is also shown that with the CSM method unbiased estimation of the directions-of-arrival (DOAs) is not possible. Inspired by the CSM algorithm, a new method for wideband array processing is developed which is based on two-sided transformation of the correlation matrices (TCT). The TCT estimator can generate unbiased estimates of the DOAs and has a lower resolution threshold than the CSM algorithm. In array processing it is frequently assumed that the signals are generated by point sources. This is an assumption which is not satis ed in reality. In this dissertation, a method is developed for localization of spatially distributed sources. The method is based on generalization of the MUSIC algorithm and is applied to coherent and incoherent distribution of sources.