CLC number: TN95
On-line Access: 2025-01-24
Received: 2024-06-02
Revision Accepted: 2024-09-30
Crosschecked: 2025-01-24
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Ke LIU, Shengfu ZHAO, Weixin CHEN, Zhen WANG, Lingxiang LI, Zhi CHEN, Qiang XU. Near-field joint estimation of multi-targets’ position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400472 @article{title="Near-field joint estimation of multi-targets’ position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition", %0 Journal Article TY - JOUR
基于张量分解的太赫兹MIMO-OFDM系统中多目标位置和速度近场联合估计1电子科技大学通信抗干扰全国重点实验室,中国成都市,611731 2西南石油大学电气工程与信息学院,中国成都市,610500 摘要:本文基于张量分解研究了近场多输入多输出(MIMO)正交频分复用(OFDM)系统中多目标位置和速度的联合估计问题。考虑各天线发送携带有通信消息且在频域中彼此正交的OFDM波形,此时的近场多目标位置和速度估计问题涉及到球面波前信号模型,其求解是极具挑战的。然而,基于球面波前的信号模型具有更高的空间位置分辨率,如果设计得当,可以用于提高参数估计精度。本文提出了一种基于CANDECOMP/PARAFAC(CP)分解的近场定位(CP-NFL)算法,用于多目标位置和速度的联合估计。该方法将接收到的信号表示为一个三阶张量;根据其因子矩阵,在此基础上将原非凸优化问题转化为凸优化问题,并使用CVX工具求解。我们的分析表明,所提出的方法可以保证CP分解的唯一性,并且计算复杂度与子载波数、OFDM符号数、天线数和目标数的三次方之和呈线性关系。仿真结果表明,相比现有方法,该方法在估计精度和计算复杂度方面都具有明显优势。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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