Article(id=1242137265573540255, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242137261979021722, articleNumber=null, orderNo=5, doi=10.3981/j.issn.1000-7857.2018.24.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1539792000000, receivedDateStr=2018-10-18, revisedDate=1541952000000, revisedDateStr=2018-11-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1547624366203, onlineDateStr=2019-01-16, pubDate=1545926400000, pubDateStr=2018-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1547624366203, onlineIssueDateStr=2019-01-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078201902, creator=sys-migrate, updateTime=1774078201902, updator=sys-migrate, issue=Issue{id=1242137261979021722, tenantId=1146029695717560320, journalId=1146031591421210625, year='2018', volume='36', issue='24', pageStart='1', pageEnd='100', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078201047, creator=sys-migrate, updateTime=1774078201047, updator=sys-migrate, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=26, endPage=32, ext={EN=ArticleExt(id=1242137270552179115, articleId=1242137265573540255, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Evolving deep neural network based multi-uav cooperative passive location with dynamic route planning, columnId=1242137266873770460, journalTitle=Science & Technology Review, columnName=Exclusive: System of Systems Engineering 2, runingTitle=null, highlight=null, articleAbstract=Aiming at the path planning problem of multiple unmanned aerial vehicles (UAVs) in passive localization, an unmanned aerial vehicle dynamic path planning method based on evolutionary depth neural network is proposed. Firstly, this method combines the differential evolution algorithm and BP neural network, and designs a learning path planning framework for UAV passive location based on evolutionary neural network. Then, angle of arrival (AOA) localization is used for the multiple UAVs, and an optimal training set is generated based on the Cramer-Rao low bound (CRLB) of target estimation. The optimized waypoints can be acquired from the learning behavior of the relative deployment between UAVs and target. Experimental results show that the unmanned aerial vehicle (UAV) based on the evolutionary neural network can greatly improve real-time performance and decrease location time., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=SVk3aqMQYOtZ+dTYXzkd9A==, pdfFileSize=3504896, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. Military Science Information Research Center, Chinese Academy of Military Sciences, Beijing 100142, China;
2. Materiel Management and UAV Engineering College, Air Force Engineering University, Xi'an 710051, China;
3. Graduate School, Air Force Engineering University, Xi'an 710051, China, fund=null, authors=YANG Junling¹, ZHOU Yu², WANG Weijia³, LI Xiangyang¹, authorsList=YANG Junling, ZHOU Yu, WANG Weijia, LI Xiangyang), CN=ArticleExt(id=1242137269197414883, articleId=1242137265573540255, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于演化深度神经网络的无人机协同无源定位动态航迹规划, columnId=1242137265871331797, journalTitle=科技导报, columnName=专题：体系工程2, runingTitle=null, highlight=null, articleAbstract=针对多无人机在无源定位过程中协同动态规划航迹提高定位精度问题，提出基于演化深度神经网络的分布式动态航迹优化方法。首先将演化计算与深层前向反馈神经网络结合，设计基于演化神经网络的无人机协同无源定位动态航迹规划框架。以多无人机到达角（AOA）协同定位为例，利用定位过程中对目标估计的克拉美罗界（CRLB）生成最优训练集。通过无人机下一时刻与目标形成的相对构型作为系统学习的行为，从而得到下一时刻优化后的航迹点。实验结果表明，该方法相对于传统中心控制的无人机协同定位方法，具有更低的处理延时，能够以更短时间达到定位精度。, correspAuthors=null, authorNote=杨俊岭(通信作者),副研究员,研究方向为军事科技信息与人工智能情报分析,电子信箱:20y02@sohu.com, correspAuthorsNote=杨俊岭(通信作者),副研究员,研究方向为军事科技信息与人工智能情报分析,电子信箱:20y02@sohu.com, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=QU8QanOdtcZT/3orstqyow==, pdfFileSize=3504896, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 军事科学院军事科学信息研究中心, 北京 100142;
2. 空军工程大学装备管理与无人机工程学院, 西安 710051;
3. 空军工程大学研究生院, 西安 710051, fund=国家自然科学基金青年基金项目（61601501，61502521）, authors=杨俊岭¹, 周宇², 王维佳³, 李向阳¹, authorsList=杨俊岭, 周宇, 王维佳, 李向阳)}, authors=null, keywords=[Keyword(id=1242137268744430046, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137265573540255, language=CN, orderNo=1, keyword=无源定位), Keyword(id=1242137268819927519, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137265573540255, language=CN, orderNo=1, keyword=航迹规划), Keyword(id=1242137268899619296, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137265573540255, language=CN, orderNo=1, keyword=动态优化), Keyword(id=1242137268991893985, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137265573540255, language=CN, orderNo=1, keyword=深度神经网络), Keyword(id=1242137269067391458, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137265573540255, language=CN, orderNo=1, keyword=演化计算), 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