Sub-Nyquist sampling of sparse and correlated signals in array processing

This paper considers efficient sampling of simultaneously sparse and correlated (S&C) signals for automotive radar application. We propose an implementable sampling architecture for the acquisition of S&C at a sub-Nyquist rate. We prove a sampling theorem showing exact and stable reconstruct...

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Vydáno v:Digital signal processing Ročník 140; s. 104125
Hlavní autoři: Ahmed, Ali, Shamshad, Fahad, Hameed, Humera
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.08.2023
Témata:
Compressive sensing
Correlation
Low-rank
Sparse
Low-rank
Correlation
Compressive sensing
Sparse
ISSN:1051-2004, 1095-4333
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Shrnutí:This paper considers efficient sampling of simultaneously sparse and correlated (S&C) signals for automotive radar application. We propose an implementable sampling architecture for the acquisition of S&C at a sub-Nyquist rate. We prove a sampling theorem showing exact and stable reconstruction of the acquired signals even when the sampling rate is smaller than the Nyquist rate by orders of magnitude. Quantitatively, our results state that an ensemble M signals, composed of a-priori unknown latent R signals, each bandlimited to W/2 but only S-sparse in the Fourier domain, can be reconstructed exactly from compressive sampling only at a rate RSlogα⁡W samples per second. When R≪M and S≪W, this amounts to a significant reduction in sampling rate compared to the Nyquist rate of MW samples per second. This is the first result that presents an implementable sampling architecture and a sampling theorem for the compressive acquisition of S&C signals. We resort to a two-step algorithm to recover sparse and low-rank (S&L) matrix from a near optimal number of measurements. This result then translates into a signal reconstruction algorithm from a sub-Nyquist sampling rate.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2023.104125