Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion

Date

2005-02-01

Author

Sayan, Gönül

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

189
views

0
downloads

This paper introduces an efficient technique to design an electromagnetic target classifier whose reference database is constructed using scattered data at only a few aspects. The suggested technique combines a natural-resonance related feature extraction process with a novel, multiaspect feature fusion scheme. First, moderately aspect-variant late-time features are extracted from scattered field of a given candidate target at several different reference aspects using the Wigner transformation to characterize the target's scattered energy distribution over a selected late-time segment of the joint time-frequency plane. Then, these features are fused using the principal component analysis to obtain a single characteristic feature vector that can effectively represent the target of concern over a broad range of aspect angles. The suggested technique is demonstrated to design a classifier that is verified to be highly accurate and robust even in the presence of excessive noise. Due to the computational efficiency of the technique, the classifier needs very small memory space to store the reference information and quite fast lending itself suitable for real-time target classification.

Subject Keywords

Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/43239

Journal

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION

DOI

https://doi.org/10.1109/tap.2004.841326

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Visual Result Prediction in Electromagnetic Simulations Using Machine Learning Karaosmanoglu, Bariscan; Ergül, Özgür Salih (Institute of Electrical and Electronics Engineers (IEEE), 2019-11-01) In this letter, we present a novel approach based on using convolutional neural networks (CNNs) to visually predict solutions of electromagnetic problems. CNN models are constructed and trained such that images of surface currents obtained at the early stages of an iterative solution can be used to predict images of the final (converged) solution. Numerical experiments demonstrate that the predicted images contain significantly better visual details than the corresponding input images. The developed approac...
Multipath Characteristics of Frequency Diverse Arrays Over a Ground Plane Cetintepe, Cagri; Demir, Şimşek (Institute of Electrical and Electronics Engineers (IEEE), 2014-07-01) This paper presents a theoretical framework for an analytical investigation of multipath characteristics of frequency diverse arrays (FDAs), a task which is attempted for the first time in the open literature. In particular, transmitted field expressions are formulated for an FDA over a perfectly conducting ground plane first in a general analytical form, and these expressions are later simplified under reasonable assumptions. Developed formulation is then applied to a uniform, linear, continuous-wave opera...
Hierarchical parallelisation strategy for multilevel fast multipole algorithm in computational electromagnetics Ergül, Özgür Salih (Institution of Engineering and Technology (IET), 2008-01-03) A hierarchical parallelisation of the multilevel fast multipole algorithm (MLFMA) for the efficient solution of large-scale problems in computational electromagnetics is presented. The tree structure of MLFMA is distributed among the processors by partitioning both the clusters and the samples of the fields appropriately for each level. The parallelisation efficiency is significantly improved compared to previous approaches, where only the clusters or only the fields are partitioned in a level.
A Hierarchical Partitioning Strategy for an Efficient Parallelization of the Multilevel Fast Multipole Algorithm Ergül, Özgür Salih (Institute of Electrical and Electronics Engineers (IEEE), 2009-06-01) We present a novel hierarchical partitioning strategy for the efficient parallelization of the multilevel fast multipole algorithm (MLFMA) on distributed-memory architectures to solve large-scale problems in electromagnetics. Unlike previous parallelization techniques, the tree structure of MLFMA is distributed among processors by partitioning both clusters and samples of fields at each level. Due to the improved load-balancing, the hierarchical strategy offers a higher parallelization efficiency than previ...
Conjugate directions based order recursive implementation of post-Doppler adaptive target detectors Candan, Çağatay (Institution of Engineering and Technology (IET), 2012-08-01) An implementation for the post-Doppler adaptive target detectors enabling an efficient change of the subspace dimension is described. The proposed implementation uses the order recursive structure of the conjugate directions method and does not present any additional computational burden on the processor. The implementation can be particularly useful for the adaptive detectors with an indeterminate number of auxiliary vectors for the clutter covariance matrix estimation. Through the proposed method, the sub...

Citation Formats

G. Sayan, “Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion,” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, pp. 766–776, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43239.