Abstract
Abstract Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this article, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, i.e., the primary signal recognition and the advanced signal recognition. In the former step, the rough k-means classifier is proposed to cluster the samples of radar emitter signals by using the rough set theory. In the latter step, the samples within the rough boundary are used to train the support vector machine (SVM). Then SVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and has a lower time complexity than the traditional approaches.
Highlights
Radar emitter recognition is a critical function in radar electronic support system, for determining the type of radar emitter [1]
A linear classifier based on the rough set and the rough k-means has been proposed, i.e., the rough k-means classifier
The initial centers for the rough k-means are computed based on the rough set, which can reduce the time complexity of the rough k-means clustering
Summary
Radar emitter recognition is a critical function in radar electronic support system, for determining the type of radar emitter [1]. Radar emitter recognition system a hybrid radar emitter recognition approach that consists of a rough k-means classifier in the primary recognition and a SVM classifier in the advanced recognition is proposed This approach is based on the fact that in the k-means clustering, the linearly inseparable samples are mostly at the margins of clusters, which makes it difficult to determine which cluster they belong to. To solve this problem, a linear classifier based on the rough k-means and a nonlinear classifier SVM are adopted. The optimal initial centers are determined by analyzing the knowledge rule of the training sample set based on rough set theory, instead of iteration.
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