Source localization using adaptive subspace beamformer outputs

Maximum likelihood (ML) parameter estimation for multi-dimensional adaptive problems is addressed. Multiple adaptive outputs are ordinarily combined by utilizing the full dimension data. However, many adaptive problems utilize subspace processing for each adaptive beam which can increase the difficulty of many super-resolution techniques. This paper shows that the steering vector structure can be utilized to allow ML techniques for a fixed grid of hypothesis vectors to be computationally feasible for many scenarios.