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  1. ; (, Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop)
    Feature extraction from wideband radio-frequency (RF) signals, such as spectral activity, interferer energy and type, or direction- of-arrival, finds use in a growing number of applications. Compressive sensing (CS)-based analog-to-information (A2I) converters enable the design of inexpensive and energy-efficient wideband RF sensing solutions for such applications. However, most A2I architectures suffer from a variety of real-world impairments. We propose a novel A2I architecture, referred to as non-uniform wavelet bandpass sampling (NUWBS). Our architecture extracts a carefully-tuned subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of most existing A2I converters. We use simulations to show that NUWBS approaches the performance limits of l1-norm-based sparse signal recovery. 
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