tailieunhanh - Báo cáo hóa học: " Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 35043 Pages 1-14 DOI ASP 2006 35043 Supervised Self-Organizing Classification of Superresolution ISAR Images An Anechoic Chamber Experiment Emanuel Radoi Andre Quinquis and Felix Totir ENSIETA E3I2 Research Center 2 rue Francois Verny 29806 Brest France Received 1 June 2005 Revised 30 January 2006 Accepted 5 February 2006 The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D multiple signal classification method coupled with two additional processing steps phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART supervised ART which is compared to two standard classifiers MLP multilayer perceptron and fuzzy KNN K nearest neighbors . While the classification accuracy is similar SART is shown to outperform the two other classifiers in terms of training speed and classification speed especially for large databases. It is also easier to use since it does not require any input parameter related to its structure. Copyright 2006 Emanuel Radoi et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Our research work has been focused for several years on ISAR techniques and automatic target recognition ATR using superresolution radar imagery. The anechoic chamber of EN-SIETA and the associated measurement .