An Adaptive Spectrogram Estimator to Enhance Signal Characterization

Time-frequency (TF) analysis is employed in numerous applications to characterize the attributes of signals. For cognitive radar it can provide valuable information regarding the particular signals/systems encountered to support automated decisionmaking. Here an adaptive approach to spectrogram estimation is considered that relies on reiterative minimum mean-square error (RMMSE) estimation. Moreover, it is experimentally shown using open-air data that the combination of adaptive direction-finding (DF) and adaptive TF analysis provides enhanced signal characterization for congested spectral environments.