Reduction of Impulse Noise by using k-mean Clustering Segmentation Approach

Chaurasia, Vishnu Kant; Amhia, Hemant

doi:10.1109/iccsp.2019.8698036

Cited by 1 publication

(1 citation statement)

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“…Haykin [17,18] first proposed the concept of cognitive radar, which was progressively transformed from the artificial cognition process of machines to an autonomous cognition process requiring no human intervention. In some cases, the process for recognizing radar emitter signals has been conducted using unsupervised learning algorithms such as the k-means clustering [19], the C-means clustering [20], the ambiguity function [21], and the density clustering algorithm [22]. However, while these methods are relatively simple and computationally efficient, the final recognition result is not ideal because these algorithms cannot adequately label the signal categories.…”

Section: Introductionmentioning

confidence: 99%

Radar Emitter Classification Based on a Multiperspective Collaborative Clustering Method and Radar Characteristic Spectrum

Wang

Zhou

et al. 2022

International Journal of Aerospace Engineering

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Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.

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Section: Introductionmentioning

confidence: 99%