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作 者:刘飞峰[1,2] 刘鸿杰 缪颖杰 李浩 胡程 LIU Feifeng;LIU Hongjie;MIAO Yingjie;LI Hao;HU Cheng(Radar Research Laboratory,School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Electronic and Information Technology in Satellite Navigation(Beijing Institute of Technology),Ministry of Education,Beijing 100081,China)
机构地区:[1]北京理工大学信息与电子学院雷达技术研究所,北京100081 [2]卫星导航电子信息技术教育部重点实验室(北京理工大学),北京100081
出 处:《信号处理》2022年第11期2276-2286,共11页Journal of Signal Processing
基 金:国家自然科学基金项目(62071045)。
摘 要:本文基于一种基于正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)的雷达通信一体化系统,利用目标时延与多普勒在时频域优良的稀疏性,提供了多种基于无网格的二维时频联合估计方法以解决传统稀疏恢复方法字典失配导致的性能较差的问题,有效提高了动目标参数估计性能,并提供了多测量向量(Multiple measurement vector,MMV)模型,可有效解决低信噪比下目标参数估计性能差的问题。同时,针对基于矢量二维原子范数计算量较大的问题,本文利用半正定规划(SDP)将传统方法的高维Toeplitz矩阵解耦为两个低维Toeplitz矩阵,可将计算复杂度降低几个数量级,同时保留原子范数在超分辨性能的优势,可适应于OFDM多子载波与多符号数波形体制。仿真结果证明该方法保持了原子范数类算法的估计性能优势,并显著减少了计算量。In this paper,an integrated radar and communication system based on Orthogonal Frequency Division Multiplexing(OFDM)is considered.And based on the excellent sparsity of target delay and Doppler in time and frequency domain,a variety of grid-less two-dimensional delay-Doppler estimation methods are provided solve the problem of poor performance caused by mismatch of estimation dictionary of traditional sparse recovery methods,effectively improving the performance of moving target parameter estimation.For the problems of poor target estimation accuracy and low recovery success rate under low SNR,this paper provides a multiple measurement vector(MMV)model to effectively solve the problem of poor target parameter estimation performance under the above problems.Aiming at the problem that the two-dimensional atomic norm based on the traditional vector calculation method will generate a huge amount of calculation.this paper uses semi-definite programming(SDP)to decouple the high-dimensional Toeplitz matrix of the traditional method into two low-dimensional Toeplitz matrices,which can reduce the computational complexity by several orders of magnitude,while retaining the advantages of atomic norm in super resolution performance.This method can be applied to OFDM multi subcarrier and multi symbol waveform systems.Numerical results show that the algorithm maintains the estimation performance advantage of atomic norm class algorithms,and simulation reduces the computational complexity.
关 键 词:雷达通信一体化 目标参数估计 无网格 原子范数最小化 压缩感知
分 类 号:TN951[电子电信—信号与信息处理]
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