Classification of the Signatures for Doppler Radar from the Standpoint of Active Perception Theory

Vasiliy E. Gai; Anna M. Bogdan

doi:10.4028/www.scientific.net/KEM.685.262

Paper Titles

Mathematical Simulation of Hydraulic Power Pulse Machine at Matlab
p.240

The Numerical Study of a Dropwise Condensation Mode when Cooling Heat Transfer Surfaces
p.244

Non-Uniform Temperature Distribution in Multilayer Thermal Protective Coating with Curved Interfaces
p.251

Numerical Simulation of Gas Evolution from Oil-in-Water Flow for Multistage Separation
p.257

Classification of the Signatures for Doppler Radar from the Standpoint of Active Perception Theory
p.262

A Three-Dimensional Boundary Element Approach for Transient Anisotropic Viscoelastic Problems
p.267

Convection Heat Transfer on a Vertical Surface in Porous Media
p.272

One-Dimensional Wave Propagation in a Three Phase Poroelastic Column
p.276

Hydrodynamics and Heat Transfer in the Coolant Flows in the Elements of the Technical Designs of Industrial Systems
p.280

HomeKey Engineering MaterialsKey Engineering Materials Vol. 685Classification of the Signatures for Doppler Radar...

Classification of the Signatures for Doppler Radar from the Standpoint of Active Perception Theory

Abstract:

This article describes the signals classification method when signals recording by land-based Doppler radar from different sources. It also presents various systems attributes defined within the active perception theory, which can be used to describe the signals. The results of computational experiments are presented.

You might also be interested in these eBooks

High Technology: Research and Applications 2015

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 685)

Pages:

262-266

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.685.262

Citation:

Cite this paper

Online since:

February 2016

Authors:

Vasiliy E. Gai*, Anna M. Bogdan

Keywords:

Active Perception Theory, Automatic Target Recognition, Digital Signal Processing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Zabalza J. et al., Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing, ISPRS J. of Photogrammetry and Remote Sensing, 93 (2014) 112-122.

DOI: 10.1016/j.isprsjprs.2014.04.006

Google Scholar

[2] Utrobin, V. A., Physical interpretation of the elements of image algebra, J. Advances in Physical Sciences 47 (2004) 1017-1032.

DOI: 10.1070/pu2004v047n10abeh001638

Google Scholar

[3] Pallotta L. et al. Pseudo-Zernike moments based radar micro-Doppler classification, Radar Conference, 2014 0850-0854.

DOI: 10.1109/radar.2014.6875709

Google Scholar

[4] M. Jahangir, K.M. Ponting, J.W. O'Loghlen, Robust Doppler classification technique based on Hidden Markov models, J. Proc. Radar Sonar Navig., 150 (2003) 33-36.

DOI: 10.1049/ip-rsn:20030027

Google Scholar

[5] I. Bilik, J. Tabrikian, A. Cohen, GMM-based target classification for ground surveillance Doppler radar, IEEE Trans. Aerospace and Electronic Systems, 42 (2006) 267-278.

DOI: 10.1109/taes.2006.1603422

Google Scholar

[6] M. Andric, B. Bondzulic, B. Zrnic, Feature Extraction Related to Target Classification for a Radar Doppler Echoes, 18th Telecom. Forum, Belgrade, Serbia, November 23-25, (2010).

Google Scholar