Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-25T01:00:38.329Z Has data issue: false hasContentIssue false
  • English
  • Français

Threat object classification with a close range polarimetric imaging system by means of H-α decomposition

Published online by Cambridge University Press:  27 March 2014

Julian Adametz  and
Lorenz-Peter Schmidt
Julian Adametz*
Affiliation:
Institute of Microwaves and Photonics (LHFT), University of Erlangen-Nuremberg (FAU), Cauerstrasse 9, 91058 Erlangen, Germany. Phone: +49 9131 85 25477
Lorenz-Peter Schmidt
Affiliation:
Institute of Microwaves and Photonics (LHFT), University of Erlangen-Nuremberg (FAU), Cauerstrasse 9, 91058 Erlangen, Germany. Phone: +49 9131 85 25477
*
Corresponding author: J. Adametz Email: julian.adametz@fau.de
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, an approach to differentiate between various dielectric threat objects in security applications is investigated. The scattering information in form of the Sinclair matrix of relevant scenarios is gained from a fully polarimetric, synthetic aperture radar. Both monostatic and multistatic array configurations are examined. A possible polarimetric calibration procedure is presented. The radar data are processed with the H-α decomposition algorithm. The H-α scattering characteristics of threat objects are analyzed in terms of a weighted averaging. It is shown that an object classification is possible even for threat objects conceiled under thick layers of clothing. Measurement results are presented to illustrate the topic.

Keywords

Radar and homeland securityQuality of life/medical diagnosis and imaging systems
Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 6 , Issue 3-4: European Microwave Week 2013 , June 2014 , pp. 415 - 421
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Sheen, D.M.; McMakin, D.L.; Hall, T.E.: Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Transact. Microw. Theory Techn., 49 (9) (2001), 1581–1592.CrossRefGoogle Scholar
[2]Nercessian, S.; Panetta, K.; Agaian, S.: Automatic detection of potential threat objects in X-ray luggage scan images, in IEEE Conf. Technologies for Homeland Security, 2008.CrossRefGoogle Scholar
[3]von Aschen, H.; Gumbmann, F.; Schmidt, L.-P.: High resolution permittivity reconstruction of one dimensional stratified dielectric media from broadband measurement data in the W-band, in Proc. 8th European Radar Conf., 2011, 45–48.Google Scholar
[4]Cenanovic, A.; Gumbmann, F.; Schmidt, L.-P.: Automated threat detection and characterization with a polarimetric multistatic imaging system, in 9th European Conf. Synthetic Aperture Radar, 2012.Google Scholar
[5]Cloude, S.R.; Pottier, E.: An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transact. Geosci. Remote Sens., 35 (1) (1997), 68–78.CrossRefGoogle Scholar
[6]Lee, J.-S.; Pottier, E.: Polarimetric Decomposition Theorem, in Polarimetric Radar Imaging, Taylor & Francis Group, Boca Raton, 2009, 229–264.Google Scholar
[7]Wiesbeck, W.; Riegger, S.: A complete error model for free space polarimetric measurements. IEEE Transact. Antennas Propag., 39 (8) (1991), 1105–1111.CrossRefGoogle Scholar
[8]Gumbmann, F.; Schmidt, L.-P.: Millimeter-wave imaging with optimized sparse periodic array for short-range applications. IEEE Transact. Geosci. Remote Sens., 49 (10) (2011), 3629–3638.CrossRefGoogle Scholar