Target recognition by self-organizing map (SOM) type unsupervised clustering using electromagnetic scattered signals in resonance region

Date

2010-12-20

Author

Sayan, Gönül
Sayan, Eren Sila

Metadata

Show full item record

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

Item Usage Stats

177
views

0
downloads

This paper investigates the use of unsupervised learning for electromagnetic target recognition in resonance region. Wigner distribution based target features extracted from late-time target responses at arbitrarily observed aspect angles are used to design a target classifier by using the self-organized map (SOM) algorithm. Effects of having unequal amounts of training data for different library targets are investigated in particular. Small scale aircraft modeled by conducting wires are used as test targets in demonstrations. © 2010 IEEE.

Subject Keywords

Unsupervised learning, Self organizing map

URI

https://hdl.handle.net/11511/57790

DOI

https://doi.org/10.1109/mmw.2010.5605193

Conference Name

10th Mediterranean Microwave Symposium

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Structural properties of ZnO binary alloy nanosystems: molecular-dynamics simulations Kılıç, Mehmet Emin; Erkoç, Şakir; Department of Physics (2015) ZnO nanostructures revealed novel implementations in optoelectronics, sensors, transducers and biomedical sciences. There are different shapes of ZnO nanostructures such as zero dimensional-0D (quantum dots, nanoparticles), one dimensional-1D (nanorods, nanowires, nanotubes) and two dimensional-2D (nanosheets) and their properties have been experimentally prepared and investigated. Thus, ZnO is one of the richest family of nanostructures among all materials, both in structures and in properties. In this the...
Multi-robot coordination control methodology for search and rescue operations Topal, Sebahattin; Erkmen, İsmet; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2011) This dissertation presents a novel multi-robot coordination control algorithm for search and rescue (SAR) operations. Continuous and rapid coverage of the unstructured and complex disaster areas in search of possible buried survivors is a time critical operation where prior information about the environment is either not available or very limited. Human navigation of such areas is definitely dangerous due to the nature of the debris. Hence, exploration of unknown disaster environments with a team of robots ...
OBJECT RECOGNITION AND LOCALIZATION WITH ULTRASONIC-SCANNING KIRAGI, H; Ersak, Aydın (1994-04-14) In this paper an object recognition and localization system based on ultrasonic range imaging to be used in optically opaque environments is introduced. The system is especially designed for robotics applications. The ultrasonic image is acquired by scanning ultrasonic transducers in two dimensions above the area where objects are located. The features that are used for recognition and localization processes are extracted from the outermost boundaries of the objects present in the input scene. Experimental ...
STATISTICAL MODELING OF THE GEOMETRIC ERROR IN CARDIAC ELECTRICAL IMAGING Aydin, Umit; Serinağaoğlu Doğrusöz, Yeşim (2009-07-01) Kalman filter approach provides a natural way to include the spatio-temporal prior information in cardiac electrical imaging. This study focuses on the performance of Kalman filter approach with geometric errors present in inverse Electrocardiography (ECG) problem. The geometric errors considered here are the wrong determination of the heart's size and location. In addition to Kalman filtering, we also compare the performances of Tikhonov regularization and Bayesian MAP estimation when geometric errors are ...
Mesh Learning for Object Classification using fMRI Measurements Ekmekci, Ömer; Ozay, Mete; Oztekin, Ilke; GİLLAM, İLKE; Oztekin, Uygar (2013-09-18) Machine learning algorithms have been widely used as reliable methods for modeling and classifying cognitive processes using functional Magnetic Resonance Imaging (fMRI) data. In this study, we aim to classify fMRI measurements recorded during an object recognition experiment. Previous studies focus on Multi Voxel Pattern Analysis (MVPA) which feeds a set of active voxels in a concatenated vector form to a machine learning algorithm to train and classify the cognitive processes. In most of the MVPA methods,...

Citation Formats

G. Sayan and E. S. Sayan, “Target recognition by self-organizing map (SOM) type unsupervised clustering using electromagnetic scattered signals in resonance region,” presented at the 10th Mediterranean Microwave Symposium, Guzelyurt, Cyprus, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57790.