A wavelet transform frequency classifier for stochastic transient signals

The paper presents a wavelet transform based frequency classifier for stochastic bandpass transient signals. Using compactly supported bases of wavelets, continuous nonstationary bandpass processes can be described by reduced dimension discrete time signal representations. We develop an optimization procedure to adapt both the sampling frequency and the discrete time wavelet filters to the continuous classes of signals involved. When compared to the classical Bayesian structures, the proposed receiver strongly reduces the required computational load. The receiver performance is accessed by Monte Carlo simulation, the probabilities of detection for each signal class, and the probability of false alarm, being computed.