Application Analysis of Compressive Sensing/Sampling Theory on Source Localization

Xue Bing Han; Chun Hui Qiu; Zhao Jun Jiang

doi:10.4028/www.scientific.net/AMM.239-240.1409

Paper Titles

Constrained All-k-Nearest-Neighbor Search
p.1387

Application of on-line Adaptive Weighted Fusion in Mine Gas Measurement System
p.1395

The Quantity Optimization of Integrated Testing Vehicle Based on Genetic Algorithm
p.1399

Detecting and Quantifying T Wave Alternans Using Linear Least Squares Curve Fitting Method
p.1404

Application Analysis of Compressive Sensing/Sampling Theory on Source Localization
p.1409

Time Series Prediction of Landslide Displacement Using SVM Model: Application to Baishuihe Landslide in Three Gorges Reservoir Area, China
p.1413

Simplified Design of Dual Quaternion Strapdown Inertial Navigation Integration Algorithms
p.1421

Research on Maintenance Resources Distribution Based on Queuing Theory
p.1428

Contourlet Transform in the Application of the Image Fusion
p.1432

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240Application Analysis of Compressive...

Application Analysis of Compressive Sensing/Sampling Theory on Source Localization

Abstract:

In this paper, we consider the source localization problem with Compressive Sensing/Sampling (CS) Theory. CS Theory asserts one can reconstruct sparse or compressible signals from a very limited number of measurements. A necessary condition relies on properties of the sensing matrix such as the restricted isometry property (RIP). This paper explains why sparse construction can be used in source localization with RIP conception.

You might also be interested in these eBooks

Measurement Technology and its Application

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

1409-1412

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.1409

Citation:

Cite this paper

Online since:

December 2012

Authors:

Xue Bing Han, Chun Hui Qiu, Zhao Jun Jiang

Keywords:

Compressive Sensing/Sampling, Direction-of-Arrival Estimation, Restricted Isometry Property, Super-Resolution

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] E. Candes and T. Tao. Decoding by linear programming: IEEE Trans. Inf. Theory. Vol. 51 (2005), p.4203–4215.

DOI: 10.1109/tit.2005.858979

Google Scholar

[2] E. J. Candes. The restricted isometry property and its implications for compressed sensing: Comptes Rendus Mathematique. Vol. 346 (2008), p.589–592.

DOI: 10.1016/j.crma.2008.03.014

Google Scholar

[3] X. Han and H. Zhang. Research of sparse recovery algorithm based on model noise. Journal of Electronics & Information Technology. Vol. 34 (2012), pp.1813-1818.

DOI: 10.3724/sp.j.1146.2011.01234

Google Scholar

[4] D. Malioutov, M. Cetin, and A. S. Willsky. A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. Signal Process. Vol. 53 (2005), p.3010–3022.

DOI: 10.1109/tsp.2005.850882

Google Scholar

[5] Harry L. Van Trees: Detection, Estimation, and Modulation Theory, Partly Ⅳ, Optimum Array Processing (New York: Wiley, 2002).

Google Scholar