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Sensors 2015, 15(4), 7136-7155;

Informational Analysis for Compressive Sampling in Radar Imaging

School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 8 January 2015 / Accepted: 19 March 2015 / Published: 24 March 2015
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [963 KB, uploaded 24 March 2015]


Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressibility of the underlying signal, relies on the trans-informational capability of the measurement matrix employed and the resultant measurements, operates with optimization-based algorithms for signal reconstruction and is thus able to complete data compression, while acquiring data, leading to sub-Nyquist sampling strategies that promote efficiency in data acquisition, while ensuring certain accuracy criteria. Information theory provides a framework complementary to classic CS theory for analyzing information mechanisms and for determining the necessary number of measurements in a CS environment, such as CS-radar, a radar sensor conceptualized or designed with CS principles and techniques. Despite increasing awareness of information-theoretic perspectives on CS-radar, reported research has been rare. This paper seeks to bridge the gap in the interdisciplinary area of CS, radar and information theory by analyzing information flows in CS-radar from sparse scenes to measurements and determining sub-Nyquist sampling rates necessary for scene reconstruction within certain distortion thresholds, given differing scene sparsity and average per-sample signal-to-noise ratios (SNRs). Simulated studies were performed to complement and validate the information-theoretic analysis. The combined strategy proposed in this paper is valuable for information-theoretic orientated CS-radar system analysis and performance evaluation. View Full-Text
Keywords: compressive sampling; rate distortion; mutual information; complex-valued scenes; radar imaging; under-sampling ratios; Gaussian mixtures compressive sampling; rate distortion; mutual information; complex-valued scenes; radar imaging; under-sampling ratios; Gaussian mixtures
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zhang, J.; Yang, K. Informational Analysis for Compressive Sampling in Radar Imaging. Sensors 2015, 15, 7136-7155.

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