Cyclostationary signal models for the detection and characterization of vibrating objects in SAR data

We present a novel method of detecting and characterizing vibrating objects in synthetic aperture radar (SAR) data. We model the SAR phase history as having cyclostationary characteristics when a vibrating object is present in the scene. Within this framework, we develop a generalized likelihood ratio test to detect the presence of the vibrating object and provide estimates of the vibration frequency, amplitude, and the spread of the vibration spectrum. We provide analytical and empirical results outlining the performance of this detection scheme.