Next Article in Journal
Synergy Effect of Combining Fluorescence and Mid Infrared Fiber Spectroscopy for Kidney Tumor Diagnostics
Next Article in Special Issue
Deep Recurrent Neural Networks for Human Activity Recognition
Previous Article in Journal
Coverage Probability and Area Spectral Efficiency of Clustered Linear Unmanned Vehicle Sensor Networks
Previous Article in Special Issue
Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2549; doi:10.3390/s17112549

Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging

1
College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130061, China
2
Institute of Automation, Shandong Academy of Sciences, Jinan 250014, China
3
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
*
Author to whom correspondence should be addressed.
Received: 26 September 2017 / Revised: 27 October 2017 / Accepted: 2 November 2017 / Published: 5 November 2017
(This article belongs to the Special Issue Sensor Signal and Information Processing)
View Full-Text   |   Download PDF [4007 KB, uploaded 7 November 2017]   |  

Abstract

The two-dimensional planar MIMO array is a popular approach for millimeter wave imaging applications. As a promising practical alternative, sparse MIMO arrays have been devised to reduce the number of antenna elements and transmitting/receiving channels with predictable and acceptable loss in image quality. In this paper, a high precision three-dimensional imaging algorithm is proposed for MIMO arrays of the regularly distributed type, especially the sparse varieties. Termed the Dimension-Factorized Range Migration Algorithm, the new imaging approach factorizes the conventional MIMO Range Migration Algorithm into multiple operations across the sparse dimensions. The thinner the sparse dimensions of the array, the more efficient the new algorithm will be. Advantages of the proposed approach are demonstrated by comparison with the conventional MIMO Range Migration Algorithm and its non-uniform fast Fourier transform based variant in terms of all the important characteristics of the approaches, especially the anti-noise capability. The computation cost is analyzed as well to evaluate the efficiency quantitatively. View Full-Text
Keywords: dimension-factorized; range migration algorithm; MIMO; regularly distributed array imaging dimension-factorized; range migration algorithm; MIMO; regularly distributed array imaging
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

Guo, Q.; Wang, J.; Chang, T.; Cui, H.-L. Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging. Sensors 2017, 17, 2549.

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