2010Unpublished venueRequires access

Real-time measurement in compressive radar imaging based on AIC

Xiaochun Xie

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Abstract

Compressive sensing techniques have been shown to reduce the number of data samples beyond the Nyquist theorem, achieving perfect reconstruction of the original signal. In this paper, we implement a real-time compressive measurement operator, based on Analog-to-Information Converter (AIC). The simulation results conform that the measurement operator works well and has as well performance as random matrix.

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What this paper is about

Compressive sensing techniques have been shown to reduce the number of data samples beyond the Nyquist theorem, achieving perfect reconstruction of the original signal. In this paper, we implement a real-time compressive measurement operator, based on Analog-to-Information Converter (AIC). The simulation results conform that the measurement operator works well and has as well performance as random matrix.

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Available abstract

Compressive sensing techniques have been shown to reduce the number of data samples beyond the Nyquist theorem, achieving perfect reconstruction of the original signal. In this paper, we implement a real-time compressive measurement operator, based on Analog-to-Information Converter (AIC). The simulation results conform that the measurement operator works well and has as well performance as random matrix.

Key concepts: Compressed sensing, Nyquist–Shannon sampling theorem, Operator (biology), Computer science, Radar, SIGNAL (programming language), Signal reconstruction, Algorithm

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