Space-time array processing: a model-based approach

Abstract

A new method of space-time array processing is introduced that is based on a model-based approach. The signal and measurement systems are placed into state-space form, thereby allowing the unknown parameters of the model, such as signal bearings, to be estimated by an extended Kalman filter. It is shown that the method outperforms the conventional beamforming approach by providing a continuously time coherent process that avoids the need for spatial and temporal discrete Fourier transforms. A major advantage of the method is that there is no inherent limitation to the degree of sophistication of the models used, and therefore it can deal with other than plane wave models, such as cylindrically or spherically spreading propagation models as well as more sophisticated representations such as the normal mode and the parabolic equation propagation models. After a theoretical exposition of the underlying theory the performance of the processor is evaluated with synthesized data sets. The results indicate that the method is a highly effective approach that is capable of significantly outperforming conventional array processors.