Intelligent CFAR Detector Based on Support Vector Machine | Zendy

Leiou Wang | Zendy; Donghui Wang | Zendy; Chengpeng Hao | Zendy

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Intelligent CFAR Detector Based on Support Vector Machine

Author(s) -

Leiou Wang,

Donghui Wang,

Chengpeng Hao

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2774262

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, we propose an intelligent constant false alarm rate detector, which uses support vector machine (SVM) techniques to improve the radar detection performance in different background environments. The proposed detector uses the variability index statistic as a feature to train a SVM and recognizes the current operational environment based on the classification results. The proposed detector has the intelligence to select the proper detector threshold adaptive to the current operational environment. This detector provides a low loss performance in homogeneous backgrounds and also performs robustly in nonhomogeneous environments including multiple targets and clutter edges.

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