Neural network-based radar signal classification system using probability moment and ApEn

Jeong, Chang Min; Jung, Young Giu; Lee, Sang Jo

doi:10.1007/s00500-017-2711-7

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…Another neural network based radar signal classification system is presented in [27]. This method introduces a novel feature extraction algorithm based on probability moment and approximate entropy (ApEn) for radar signal classification.…”

Section: A Review Of Current Trends Of Application Of Ai Based Technmentioning

confidence: 99%

Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications

2020

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Section: A Review Of Current Trends Of Application Of Ai Based Technmentioning

confidence: 99%

Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications

2020

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“…Experimental results show that the proposed system had efficient performance compared with traditional clustering algorithms regarding time and spatial complexity. Jeong et al (2017) proposed a new approach for the development of high performance on radar signal classifier based on the probability moment and ApEn. The proposed method is based on a signal that is given similar value.…”

Section: Related Workmentioning

confidence: 99%

Advances in intelligence and internet of things for human-centric computing

Pan

Park

2018

Soft Comput

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“…Conventional template-matching and knowledgebased methods: in a template-matching method (Zhang, 2003), pulse descriptors were measured by the sensor and the detection system, and the operating mode was identified by comparing the descriptors and the template. However, with the enhancement of radar function and the increase of electronic environment signal density, the identification accuracy of template matching can be seriously reduced (Jeong et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A novel radar operating mode identification approach based on variational relevance vector machine with chaotic gravitational search optimization

Chen

Wang

Zou³

et al. 2022

Transactions of the Institute of Measurement and Control

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Radar operating mode identification is one of the top priorities in the electronic countermeasure field for threat prediction. However, the inference speed and accuracy of existing identification methods are still not satisfactory for threat detection, and some of them need extra prior knowledge to extract features. This paper introduces an identification approach for radar operating mode to solve these problems. The variational relevance vector machine (VRVM) is presented as the feature extractor and the basic unit of identification. An improved chaotic gravity search algorithm (CGSA) is developed to increase the search breadth and optimize the hyperparameters of VRVM. Threshold-one-versus-one (TOVO) is further proposed to screen the identification results to form the final ensemble method called CGSA–VRVM–TOVO. Experimental results indicate the CGSA–VRVM–TOVO achieves 99.44% accuracy without prior knowledge. Compared with XGBoost, Random Forest, and LightGBM, our approach is 1.94%, 0.55%, and 1.11% higher in accuracy, respectively, and twice as fast as the fastest method of them all.

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Neural network-based radar signal classification system using probability moment and ApEn

Cited by 12 publications

References 11 publications

Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications

Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications

Advances in intelligence and internet of things for human-centric computing

A novel radar operating mode identification approach based on variational relevance vector machine with chaotic gravitational search optimization

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