ABSTRACT
High-resolution methods are generally defined to be high-performance methods for estimating and/or detecting the desired and/or undesired signal components present in a given set of data. The term "high-resolution" also implies a good ability to resolve very "similar" signal components. One of the most common problems in signal processing is known as frequency estimation. This chapter presents several frequency estimation techniques using algebraic principles. They are linear prediction, matrix pencil, and iterative quadratic maximum likelihood. The chapter presents methods that exploit large sample theorems in statistics, and also resents several detection methods using a single data measurement. It considers multiple data measurements for signal detection, and reviews the Akaike information criterion (AIC) and the minimum description length (MDL) methods. For an optimal accuracy of estimation, the maximum likelihood (ML) method is the classical choice. But the computational burden of the ML method can be prohibitive.
