Results 241 to 250 of about 482 (253)
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Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting
Journal of Central South University, 2014In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio
Yi-bing Li, Juan Ge, Yun Lin, Fang Ye
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2004
Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (
Gexiang Zhang, Laizhao Hu, Weidong Jin
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Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (
Gexiang Zhang, Laizhao Hu, Weidong Jin
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Recognition for Radar Emitter Signals Based on Bispectral Feature Fusion
2022 5th International Conference on Electronics and Electrical Engineering Technology (EEET), 2022Jundi Wang +4 more
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Intra-pulse Modulation Recognition of Unknown Radar Emitter Signals Using Support Vector Clustering
2006Unknown radar emitter signal (RES) recognition is an important issue in modern electronic warfare because the enemy's RESs are usually uncertain in the battlefield. Although unsupervised classifiers are used generally in many domains, few literatures deal with applications of unsupervised classifiers to RES recognition.
Gexiang Zhang, Haina Rong, Weidong Jin
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2006
A new method is proposed to solve the difficult problem of advanced radar emitter signal (RES) recognition. Different from traditional five-parameter method, the method is composed of feature extraction, feature selection using rough set theory and combinatorial classifier.
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A new method is proposed to solve the difficult problem of advanced radar emitter signal (RES) recognition. Different from traditional five-parameter method, the method is composed of feature extraction, feature selection using rough set theory and combinatorial classifier.
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2006
Classifier design is an important issue in radar emitter signal (RES) recognition in which respondence time is a very important and strict performance criterion. For computational efficiency, the multiclass support vector machines (SVMs) with binary tree architecture is introduced to recognize advanced RESs.
Gexiang Zhang, Haina Rong, Weidong Jin
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Classifier design is an important issue in radar emitter signal (RES) recognition in which respondence time is a very important and strict performance criterion. For computational efficiency, the multiclass support vector machines (SVMs) with binary tree architecture is introduced to recognize advanced RESs.
Gexiang Zhang, Haina Rong, Weidong Jin
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2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR), 2023
Jie Yang 0061, Junpeng Zhu, Weilin Zhu
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Jie Yang 0061, Junpeng Zhu, Weilin Zhu
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An automatic data cleaning method for radar emitter signal recognition
Sixteenth International Conference on Signal Processing Systems (ICSPS 2024)Zehuan Jing +6 more
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Radar emitter signal recognition based on feature fusion and machine learning
International Conference on Image, Signal Processing, and Machine Learning (ISPML 2025)Jiazheng Zhong +4 more
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