{"title":"子阵天线最优划分设计的改进遗传算法:多目标遗传算法","authors":"G. Golino","doi":"10.1109/RADAR.2005.1435903","DOIUrl":null,"url":null,"abstract":"In this paper a novel approach to the optimisation of the ECCM (electronic counter measures) capabilities of a phased array radar has been proposed. The division in sub-arrays for adaptive digital beamforming is performed by a MOGA (multi-objective genetic algorithm) which aims to find optimal trade-offs between competitive objectives (detection probability, accuracy of the target angular estimation, level of the side lobes, etc.). The solutions obtained after several cycles of the algorithm for a study case (square phased array antenna with 64 radiating elements) are presented.","PeriodicalId":444253,"journal":{"name":"IEEE International Radar Conference, 2005.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Improved genetic algorithm for the design of the optimal antenna division in sub-arrays: a multi-objective genetic algorithm\",\"authors\":\"G. Golino\",\"doi\":\"10.1109/RADAR.2005.1435903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a novel approach to the optimisation of the ECCM (electronic counter measures) capabilities of a phased array radar has been proposed. The division in sub-arrays for adaptive digital beamforming is performed by a MOGA (multi-objective genetic algorithm) which aims to find optimal trade-offs between competitive objectives (detection probability, accuracy of the target angular estimation, level of the side lobes, etc.). The solutions obtained after several cycles of the algorithm for a study case (square phased array antenna with 64 radiating elements) are presented.\",\"PeriodicalId\":444253,\"journal\":{\"name\":\"IEEE International Radar Conference, 2005.\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Radar Conference, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RADAR.2005.1435903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Radar Conference, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR.2005.1435903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved genetic algorithm for the design of the optimal antenna division in sub-arrays: a multi-objective genetic algorithm
In this paper a novel approach to the optimisation of the ECCM (electronic counter measures) capabilities of a phased array radar has been proposed. The division in sub-arrays for adaptive digital beamforming is performed by a MOGA (multi-objective genetic algorithm) which aims to find optimal trade-offs between competitive objectives (detection probability, accuracy of the target angular estimation, level of the side lobes, etc.). The solutions obtained after several cycles of the algorithm for a study case (square phased array antenna with 64 radiating elements) are presented.
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