Next Article in Journal
Detection-Response Task—Uses and Limitations
Previous Article in Journal
On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(2), 593; https://doi.org/10.3390/s18020593

Robust Automatic Target Recognition via HRRP Sequence Based on Scatterer Matching

1
Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2
Beijing Key Laboratory of Embedded Real-time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 4 January 2018 / Accepted: 13 January 2018 / Published: 14 February 2018
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [4379 KB, uploaded 14 February 2018]   |  

Abstract

High resolution range profile (HRRP) plays an important role in wideband radar automatic target recognition (ATR). In order to alleviate the sensitivity to clutter and target aspect, employing a sequence of HRRP is a promising approach to enhance the ATR performance. In this paper, a novel HRRP sequence-matching method based on singular value decomposition (SVD) is proposed. First, the HRRP sequence is decoupled into the angle space and the range space via SVD, which correspond to the span of the left and the right singular vectors, respectively. Second, atomic norm minimization (ANM) is utilized to estimate dominant scatterers in the range space and the Hausdorff distance is employed to measure the scatter similarity between the test and training data. Next, the angle space similarity between the test and training data is evaluated based on the left singular vector correlations. Finally, the range space matching result and the angle space correlation are fused with the singular values as weights. Simulation and outfield experimental results demonstrate that the proposed matching metric is a robust similarity measure for HRRP sequence recognition. View Full-Text
Keywords: automatic target recognition (ATR); high resolution range profile (HRRP); singular value decomposition (SVD); atomic norm minimization (ANM); feature extraction automatic target recognition (ATR); high resolution range profile (HRRP); singular value decomposition (SVD); atomic norm minimization (ANM); feature extraction
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).

Share & Cite This Article

MDPI and ACS Style

Jiang, Y.; Li, Y.; Cai, J.; Wang, Y.; Xu, J. Robust Automatic Target Recognition via HRRP Sequence Based on Scatterer Matching. Sensors 2018, 18, 593.

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