Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Asymptotic achievability of the Cramér-Rao bound for noisy compressive sampling
IEEE Transactions on Signal Processing
Necessary and sufficient conditions for sparsity pattern recovery
IEEE Transactions on Information Theory
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
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Multi-sensor receivers are commonly tasked with detecting, demodulating and geolocating target emitters over very wide frequency bands. Compressed sensing can be applied to persistently monitor a wide bandwidth, given that the received signal can be represented using a small number of coefficients in some basis. In this paper we present a multi-sensor compressive sensing receiver that is capable of reconstructing frequency-sparse signals using block reconstruction techniques in a sensor-frequency basis. We derive performance bounds for time-difference and angle of arrival (AoA) estimation of such a receiver, and present simulated results in which we compare AoA reconstruction performance to the bounds derived.