Target assignment and power allocation for LPI radar networks

Seyed Mehdi Hosseini Andargoli, Javad Malekzadeh
{"title":"Target assignment and power allocation for LPI radar networks","authors":"Seyed Mehdi Hosseini Andargoli, Javad Malekzadeh","doi":"10.1109/AISP.2015.7123480","DOIUrl":null,"url":null,"abstract":"In this paper, power allocation and target assignment is considered as a promising way to obtain low probability of interception (LPI) in the radar network. Spatial diversity in the netted radars gives us a flexibility to control power intelligently and radar assignment dynamically in such a way that not only detection performances satisfied but LPI characteristics of the network are optimized. We formulate this problem as a non-convex and nonlinear optimization problem associated with detection performance constraints. The optimum solution of general problem is complicated and cannot be solved mathematically. We relaxed problem to more tractable form for the networks with low complexity in which combination of radar's information cannot be handled. In the considered scenario, for each target only one radar is assigned and each assigned radar can only transmit one target's information. We propose a simple framework to obtain optimum power allocation and radar assignment strategy due to spatial diversity of netted radars. The framework has lower complexity compared with optimum exhaustive search algorithm and simulation results show effectiveness of proposed algorithm in satisfaction of detection performances and improvement of LPI specification of radar network.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AISP.2015.7123480","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

In this paper, power allocation and target assignment is considered as a promising way to obtain low probability of interception (LPI) in the radar network. Spatial diversity in the netted radars gives us a flexibility to control power intelligently and radar assignment dynamically in such a way that not only detection performances satisfied but LPI characteristics of the network are optimized. We formulate this problem as a non-convex and nonlinear optimization problem associated with detection performance constraints. The optimum solution of general problem is complicated and cannot be solved mathematically. We relaxed problem to more tractable form for the networks with low complexity in which combination of radar's information cannot be handled. In the considered scenario, for each target only one radar is assigned and each assigned radar can only transmit one target's information. We propose a simple framework to obtain optimum power allocation and radar assignment strategy due to spatial diversity of netted radars. The framework has lower complexity compared with optimum exhaustive search algorithm and simulation results show effectiveness of proposed algorithm in satisfaction of detection performances and improvement of LPI specification of radar network.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
LPI雷达网络的目标分配与功率分配
本文认为功率分配和目标分配是雷达网络中实现低截获概率的一种很有前途的方法。网络雷达的空间分异使我们能够灵活地智能控制功率和动态分配雷达,从而在满足探测性能的同时优化网络的LPI特性。我们将此问题表述为与检测性能约束相关的非凸非线性优化问题。一般问题的最优解比较复杂,无法用数学方法求解。对于无法处理雷达信息组合的低复杂度网络,我们将问题简化为更易于处理的形式。在所考虑的场景中,对于每个目标只分配一个雷达,每个分配的雷达只能传输一个目标的信息。我们提出了一个简单的框架,以获得最佳的功率分配和雷达分配策略,由于网络雷达的空间多样性。与最优穷举搜索算法相比,该框架具有较低的复杂度,仿真结果表明该算法在满足雷达网络检测性能和提高LPI规格方面是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Small target detection and tracking based on the background elimination and Kalman filter A novel image watermarking scheme using blocks coefficient in DHT domain Latent space model for analysis of conventions A new algorithm for data clustering based on gravitational search algorithm and genetic operators Learning a new distance metric to improve an SVM-clustering based intrusion detection system
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1