Detection and classification arise in signal processing problems whenever a decision is to be made
among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms
decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal
classification algorithms decide whether a detected signal belongs to one or another of prespecified
classes of signals. The objective of signal detection and classification theory is to specify systematic
strategies for designing algorithms which minimize the average number of decision errors. This
theory is grounded in the mathematical discipline of statistical decision theory where detection and
classification are respectively called binary and M-ary hypothesis testing...