Informational Analysis for Compressive Sampling in Radar Imaging
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/4/7136 |
| _version_ | 1828358652801581056 |
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| author | Jingxiong Zhang Ke Yang |
| author_facet | Jingxiong Zhang Ke Yang |
| author_sort | Jingxiong Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:31:57Z |
| format | Article |
| id | doaj.art-66c241e621b044479daca1140220a559 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:31:57Z |
| publishDate | 2015-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-66c241e621b044479daca1140220a5592022-12-22T02:14:54ZengMDPI AGSensors1424-82202015-03-011547136715510.3390/s150407136s150407136Informational Analysis for Compressive Sampling in Radar ImagingJingxiong Zhang0Ke Yang1School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, 430079 Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, 430079 Wuhan, ChinaCompressive 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.http://www.mdpi.com/1424-8220/15/4/7136compressive samplingrate distortionmutual informationcomplex-valued scenesradar imagingunder-sampling ratiosGaussian mixtures |
| spellingShingle | Jingxiong Zhang Ke Yang Informational Analysis for Compressive Sampling in Radar Imaging Sensors compressive sampling rate distortion mutual information complex-valued scenes radar imaging under-sampling ratios Gaussian mixtures |
| title | Informational Analysis for Compressive Sampling in Radar Imaging |
| title_full | Informational Analysis for Compressive Sampling in Radar Imaging |
| title_fullStr | Informational Analysis for Compressive Sampling in Radar Imaging |
| title_full_unstemmed | Informational Analysis for Compressive Sampling in Radar Imaging |
| title_short | Informational Analysis for Compressive Sampling in Radar Imaging |
| title_sort | informational analysis for compressive sampling in radar imaging |
| topic | compressive sampling rate distortion mutual information complex-valued scenes radar imaging under-sampling ratios Gaussian mixtures |
| url | http://www.mdpi.com/1424-8220/15/4/7136 |
| work_keys_str_mv | AT jingxiongzhang informationalanalysisforcompressivesamplinginradarimaging AT keyang informationalanalysisforcompressivesamplinginradarimaging |