Van Veen This research involves the development and evaluation of efficient, high performance signal processing algorithms for signal estimation and detection. Algorithms for processing data collected at arrays of sensors and for analysis of time series are of particular interest. One technique for reducing the complexity and improving the performance of signal processing algorithms is based on mapping data into subspaces prior to processing. Mapping of data into subspaces is appropriate for almost all signal processing problems, and is especially applicable, if not mandatory, to problems in which large quantities of data must be processed. A key issue under study is the design of linear transformations which maximize performance while minimizing subspace dimension. Processing of data mapped into subspaces is being explored in adaptive beamforming, adaptive filtering, spectrum estimation, and source location estimation problems, as well as in more general nonlinear signal processing algorithms. Determination of appropriate performance criteria for transformation design and tradeoffs between performance and complexity are under investigation. Statistical analysis and simulation are being utilized to analyze the performance of the resulting algorithms.
