Modern Electronic Warfare Signal Processing Based on Analog-to-Information Conversion

Many problems in radar signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to sample these signals by the Shannon/Nyquist sampling theorem. However, the information level of the signal is often far lower than the actual bandwidth, while the novel theory of compressive sensing enables the direct analog-to-information converter (AIC) of compressible signals at sub-Nyquist acquisition rates. In this paper we propose a system that uses a parallel bank of low-rate analog-to-digital converter (ADC) to produce a low-rate set of digital measurements. Our AIC is inspired by the recent theory of Compressive Sensing (CS). We generalize the CS theory to fit it into the continuous-time sparse signal backdrop, explain AIC system in the CS context, and discuss practical issues regarding implementation. Simulation shows that the architecture is particularly apropos for wideband signals that are sparse or nearly sparse in the time-frequency domain.