Multi-Channel and Multi-Dimensional Sensors Parametric Statistics Estimation.

摘要

Parametric estimation, when expressed in the form of AR models, linear prediction analysis, lattice filters or minimum variance adaptive filters, has been a major foundation for enhanced signal processing performance. Examples include high resolution spectral analysis, linear prediction coding for bit rate reduction, and enhanced detection statistic performance for space-time adaptive processing (STAP) using parametric forms of the inverse covariance matrix. The mathematical matrix structures of most parametric estimators are typically exploitable, leading to fast computational algorithms that reduce the computational burden by typically one to two orders of magnitude, making the parametric algorithms competitive with more traditional non-parametric spectrum and filter applications. Parametric algorithms developed to date are 1-D single channel, 1-D multi-channel one dimension is usually time, and 2-D. Currently published 2-D fast algorithms are only efficient along one dimension because the other dimension is assumed fixed. This project will use further exploitable structure to lead to even faster 1-D and 2-D parametric estimation computations.