Reducing Data Dimension to Lower Signal Processing Computational Requirements and Maximize Performance.

摘要

The problem studied concerns reducing the dimension of data by mapping it through rectangular matrix transformations before application of signal processing algorithms. Our work addressed applications of this principle in adaptive beamforming, spectral estimation, and detection problems. While dimension reduction often leads to dramatic reductions in the computational burden of the signal processing algorithm, it can also introduce significant asymptotic performance losses if the transformation is not chosen properly. We choose dimension reducing transformations to optimize performance criteria associated with the problem of interest. Our results indicate that dramatic reductions in dimension can be achieved with relatively small asymptotic performance losses using these design procedures. Performance analyses demonstrate that dimension reduction is most profitably used in applications where relatively short data records are available or fast response time is required. In these cases dimension reduction actually improves performance.... Partially adaptive beamforming, Minimum variance spectrum analysis, Adaptive detection, Beamspace processing, Dimension reduction.