Next Article in Journal
Squeeze-Film Air Damping of a Five-Axis Electrostatic Bearing for Rotary Micromotors
Previous Article in Journal
Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor—A Solution for Smoothing the Output Power of PV Power Plants
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 1120; doi:10.3390/s17051120

Sparsity Adaptive Matching Pursuit Detection Algorithm Based on Compressed Sensing for Radar Signals

1
College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
2
College of Science, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jonathan Li
Received: 20 February 2017 / Revised: 4 May 2017 / Accepted: 11 May 2017 / Published: 13 May 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [473 KB, uploaded 15 May 2017]   |  

Abstract

In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms. View Full-Text
Keywords: compressed sensing; radar signal; sparsity adaptive; target detection compressed sensing; radar signal; sparsity adaptive; target detection
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wei, Y.; Lu, Z.; Yuan, G.; Fang, Z.; Huang, Y. Sparsity Adaptive Matching Pursuit Detection Algorithm Based on Compressed Sensing for Radar Signals. Sensors 2017, 17, 1120.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top