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|Title:||Ground Target Recognition using KNN,Naïve Baye’s and SVM|
|Supervisor:||Bhalla, Vinod Kumar|
|Keywords:||Feature extraction;Radar Signal;PCA;ATR|
|Abstract:||Building an effective classification model when the high dimensional data is given that is taken from radar signals is a major challenge to solve this problem for automatic target recognition community (ATR). The problem is severe when pictures have taken from different azimuth angles. To surmount this classification problem and high dimensionality issues in the dataset, we propose a framework that comprises of Principal Component Analysis (PCA), which are used for feature extraction, as well as feature ranker of data. As we also know that principal component analysis is widely used from in various field like space science. The main purpous of using principal component analysis is data compression and feature extraction. This framework is developed using Python language and various Python packages. Then the performance of proposed framework is evaluated and found that proposed framework gives better results than other methods.|
|Appears in Collections:||Masters Theses@CSED|
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