Method and Apparatus for Spectral Cross Coherence

Spectral analysis is a valuable tool in the extraction of useful information from various kinds of data. The process often involves the use of spectral estimation filters, which traditionally have limitations in terms of resolution and accuracy. With advancements in technology and data science, there is a growing need for more robust tools and techniques that can leverage the potential of data in the most efficient manner. Presently, methods involving the usage of estimator filters, like the Bartlett and Capon estimators, have been rather limited in their ability to produce high-resolution results consistently. There are also challenges around determining the degree of coherence between data sets when different estimation filters are applied, restricting the potential of advanced data analysis. In addition, prevalent models like the MUSIC (multiple signal classification) algorithm largely rely on finite samples, limiting some uses.

Technology Description

This patented technology pertains to a machine-implemented method for spectral analysis. The method focuses on identifying a level of cross coherence between data processed through two different spectral estimation filters. Unique identifiers— or spectral features— are extracted from this measure of cross coherence for further use. An exemplary embodiment of this invention also provides an in-depth statistical summarization of the dependence between Bartlett and Capon spectral statistics— a relationship that can be illustrated via a 2 x 2 complex Wishart matrix. The provided system differentiates itself through its innovative approach of determining the degree of cross coherence, which, in turn, informs the invention’s unique two-dimensional algorithm. This algorithm is reported to give much better resolution than the commonly used Capon algorithm, occasionally even surpassing results derived from the finite-sample-reliant multiple signal classification (MUSIC) algorithm. This pioneering approach provides nuanced data analysis, thus enhancing the quality of spectral results.