Abstract

Traditional radar sensing typically employs matched filtering between the received signal and the shape of the transmitted pulse. Matched filtering is conventionally carried out digitally, after sampling the received analog signals. Here, principles from classic sampling theory are generally employed, requiring that the received signals be sampled at twice their baseband bandwidth. The resulting sampling rates necessary for correlation based radar systems become quite high, as growing demands for target distinction capability and spatial resolution stretch the bandwidth of the transmitted pulse. The large amounts of sampled data also necessitate vast memory capacity. In addition, real-time processing of the data typically results in high power consumption. Recently, new approaches for radar sensing and estimation were introduced, based on the Finite Rate of Innovation and Xampling frameworks. Exploiting the parametric nature of the radar problem, these techniques allow significant reduction in sampling rate, implying potential power savings, while maintaining the systems estimation capabilities at sufficiently high signal-to-noise ratios. Here we present for the first time a design and implementation of a Xampling based hardware prototype that allows sampling of radar signals at rates much lower than Nyquist. We demonstrate by real-time analog experiments that our system is able to maintain reasonable recovery capabilities, while sampling radar signals that require sampling at a rate of about 30MHz at a total rate of 1MHz.