Darbeli radarlarda hedef sınıflama için AR modelinin güç spektrumu ve yapay sinir ağı temelli özellik çıkarma yöntemi

Bu çalışmada, örüntü tanımanın en önemli kısmı olan özellik çıkarma için geliştirilmiş etkili bir yöntem sunulmuştur. Darbeli radarlarda hedef sınıflama için AR modelinin güç spektrumu ve yapay sinir ağı temelli, yeni bir özellik çıkarma yöntemi geliştirilmiştir. Bu yöntemle ölçülen darbeli radar işaretlerinin AR modeli güç spektrumundan elde edilen 512 adet özellik kullanılarak sınıflama yapılmaktadır. Özelliklerin frekans bölgesinden elde edilmesi ve özellik sayısının fazla oluşu yöntemin güvenirliğini ve etkinliğini yükseltmektedir. Sınıflama yapısı ileri beslemeli ve geri yayınım öğrenme algoritmalı yapay sinir ağı üzerine kurulmuştur. Böylelikle akıllı ve otomatik bir sınıflama gerçekleşmesi sağlanmıştır.

Feature extraction method based on power spektrum of AR model and artificial neural network for target classification in the pulse radars

In this study, we present an efficient method for feature extraction, which is the most important stage of pattern recognition. We develop a new feature extraction procedure based on power spectrum of AR models and artificial neural network for target classification in the pulse radars. This method makes classification using 512 features obtained from power spectrum of AR models of measured pulse radar signals. Efficiency and reliability of the method is high because the features are obtained in the frequency domain and the number of the features is high. Structure of classification is set up based on artificial neural network of feed forward and the back propagation-learning algorithm. In this way, an automatic and intelligent classification is realised.

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