M. E. A. Kanona, Akram Mohamed Ahmed, Majzoub Emad Mirghani, Mohamed Khalafalla Hassan, A. Abdalla, Mohammed Elghazali Hamza
{"title":"基于神经网络的FSR地面目标分类混合预处理技术性能分析","authors":"M. E. A. Kanona, Akram Mohamed Ahmed, Majzoub Emad Mirghani, Mohamed Khalafalla Hassan, A. Abdalla, Mohammed Elghazali Hamza","doi":"10.1109/ICCCEEE49695.2021.9429647","DOIUrl":null,"url":null,"abstract":"This paper presents an enhanced pattern recognition neural network classifier for ground target classification in FSR. The hybrid enhanced model is based on a different combination of pre-processing techniques. Wavelet and feature extraction techniques were applied on time-domain raw data of three vehicles before it is processed by the neural network classifier. Several scenarios were undertaken to study and analyze the effect of adding the proposed techniques to the NN classifier’s performance. The result reveals the potential and effectiveness of pre-processing to increase the performance by 79.2% to previous work, which gives more than 97% true classification.","PeriodicalId":359802,"journal":{"name":"2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Performance Analysis of Hybrid Pre-Processing Techniques of Ground Target Classification in FSR using Neural Network\",\"authors\":\"M. E. A. Kanona, Akram Mohamed Ahmed, Majzoub Emad Mirghani, Mohamed Khalafalla Hassan, A. Abdalla, Mohammed Elghazali Hamza\",\"doi\":\"10.1109/ICCCEEE49695.2021.9429647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an enhanced pattern recognition neural network classifier for ground target classification in FSR. The hybrid enhanced model is based on a different combination of pre-processing techniques. Wavelet and feature extraction techniques were applied on time-domain raw data of three vehicles before it is processed by the neural network classifier. Several scenarios were undertaken to study and analyze the effect of adding the proposed techniques to the NN classifier’s performance. The result reveals the potential and effectiveness of pre-processing to increase the performance by 79.2% to previous work, which gives more than 97% true classification.\",\"PeriodicalId\":359802,\"journal\":{\"name\":\"2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)\",\"volume\":\"123 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCEEE49695.2021.9429647\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCEEE49695.2021.9429647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance Analysis of Hybrid Pre-Processing Techniques of Ground Target Classification in FSR using Neural Network
This paper presents an enhanced pattern recognition neural network classifier for ground target classification in FSR. The hybrid enhanced model is based on a different combination of pre-processing techniques. Wavelet and feature extraction techniques were applied on time-domain raw data of three vehicles before it is processed by the neural network classifier. Several scenarios were undertaken to study and analyze the effect of adding the proposed techniques to the NN classifier’s performance. The result reveals the potential and effectiveness of pre-processing to increase the performance by 79.2% to previous work, which gives more than 97% true classification.