AM-FM ENERGY DETECTION AND SEPARATION IN NOISE USING MULTIBAND ENERGYOPERATORS

This paper develops a multiband or wavelet approach for capturing the AM-FM components of modulated signals immersed in noise. The technique utilizes the recently-popularized nonlinear energy operator Psi(s) = (s)(2) - ss to isolate the AM-FM energy, and an energy separation algo rithm (ESA) to extract the instantaneous amplitudes and frequencies. I t is demonstrated that the performance of the energy operator/ESA appr oach is vastly improved if the signal is first filtered through a bank of bandpass filters, and at each instant analyzed (via Psi and the ES A) using the dominant local channel response. Moreover, it is found th at uniform (worst-case) performance across the frequency spectrum is a ttained by using a constant-Q, or multiscale wavelet-like filter bank. The elementary stochastic properties of Psi and of the ESA are develo ped first. The performance of Psi and the ESA when applied to bandpass filtered versions of an AM-FM signal-plus-noise combination is then a nalyzed. The predicted performance is greatly improved by filtering, i f the local signal frequencies occur in-band. These observations motiv ate the multiband energy operator and ESA approach, ensuring the in-ba nd analysis of local AM-FM energy. In particular, the multi-bands must have the constant-Q or wavelet scaling property to ensure uniform per formance across bands. The theoretical predictions and the simulation results indicate that improved practical strategies are feasible for t racking and identifying AM-FM components in signals possessing pattern coherencies manifested as local concentrations of frequencies.