Electromagnetic Signal Processing for Feature Extraction and Classification of Lossy Dielectric Targets

2013-08-15
This paper presents the application of a real-time electromagnetic target classification technique to recognize dispersive dielectric objects with varying loss characteristics. The suggested classifier design technique is operative at the resonance region and is based on the use of Singularity Expansion Method (SEM) to represent a given electromagnetic scatterer by its natural response. A multi-aspect database of wide band scattered signals are processed to extract target features with the ultimate aim of target characterization in an aspect invariant manner. The Wigner distribution (WD), a quadratic time-frequency transformation, is used to extract pole-related feature vectors from non-stationary scattered signals. The Principal Components Analysis (PCA) is further used to fuse multi-aspect feature vectors to obtain a single unified feature vector for each library object. A suitable late-time design interval needs to be chosen to obtain fused features with minimized aspect sensitivity that is a vital requirement for improved classifier accuracy.
Progress In Electromagnetics Research Symposium

Suggestions

Electromagnetic Object Recognition for Dielectric Coated Conductors Based on WD-PCA Type Fused Feature Extraction
Sayan, Gönül (2012-01-01)
This paper presents the application of a real-time electromagnetic object classification technique to recognize dielectric coated conducting objects using their wideband scattered signals received at arbitrary combinations of aspect and polarization. The suggested classifier design technique depends on the use of Singularity Expansion Method (SEM) to represent a given electromagnetic scatterer by its natural resonances. A quadratic time-frequency transformation, the Wigner distribution (WD), is used to extr...
Electromagnetic Target Classification using time frequency analysis and neural networks
Sayan, Gönül; Leblebicioğlu, Mehmet Kemal (Wiley, 1999-04-01)
This paper demonstrates the feasibility and advantages of using a self-organizing map (SOM)-type neural network classifier for electromagnetic target recognition. The classifier is supported by a novel feature extraction unit in which the Wigner distribution (WD), a time-frequency representation, is utilized for the extraction of natural-resonance-related energy feature vectors from scattered fields. The proposed target classification technique is tested for a set of canonical targets, displaying an excelle...
Electromagnetic target classification of small-scale aircraft modeled by conducting wire structures using a natural resonance based feature extraction technique
Ersoy, Mehmet Okan; Sayan, Gönül (2005-12-01)
The problem studied in this paper is the design of an electromagnetic target classifier for small-scale aircraft targets by using a natural resonance based feature extraction technique supported by feature fusion. The aircraft targets are modeled by perfectly conducting straight thin wire structures and the electromagnetic fields back-scattered from targets are numerically generated. This technique uses the Wigner-Ville distribution (WD) and the principal component analysis (PCA). The technique is applied t...
Numerical modeling and optimization of HgCdTe infrared photodetectors for thermal imaging
Koçer, Hasan; Beşikci, Cengiz; Department of Electrical and Electronics Engineering (2011)
This thesis presents a detailed investigation of the performance limiting factors of long wavelength infrared (LWIR) and very long wavelength infrared (VLWIR) p on n HgCdTe detectors through numerical simulations at 77 K incorporating all considerable generation-recombination mechanisms including trap assisted tunneling (TAT), Shockley-Read-Hall (SRH), Auger and radiative processes. Numerical simulations under dark and illuminated conditions were performed with different absorber layer thicknesses, material...
Electromagnetic target recognition with the fused MUSIC spectrum matrix method: Applications and performance analysis for incomplete frequency data
Secmen, Mustafa; Ekmekci, Evren; Sayan, Gönül (2007-01-01)
The aim of this paper is to apply an electromagnetic target recognition method, which is based on the use of fused MUSIC spectrum matrices, to the case of incomplete frequency domain data. The aforementioned method was suggested recently and succesfully applied to both canonical and complicated targets in the presence of complete frequency domain data [1]. However, most of the real world applications involve the use of severely incomplete frequency data, especially missing low frequency information. In this...
Citation Formats
G. Sayan, “Electromagnetic Signal Processing for Feature Extraction and Classification of Lossy Dielectric Targets,” presented at the Progress In Electromagnetics Research Symposium, Stockholm, SWEDEN, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55936.