Suppr超能文献

一种用于多目标多输入多输出(MOTS MIMO)雷达的联合角度估计的单一旋转不变技术(ESPRIT)方案。

A unitary ESPRIT scheme of joint angle estimation for MOTS MIMO radar.

作者信息

Wen Chao, Shi Guangming

机构信息

State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University, No.2 Taibai South Road, Xi'an 710071, China.

出版信息

Sensors (Basel). 2014 Aug 7;14(8):14411-22. doi: 10.3390/s140814411.

Abstract

The transmit array of multi-overlapped-transmit-subarray configured bistatic multiple-input multiple-output (MOTS MIMO) radar is partitioned into a number of overlapped subarrays, which is different from the traditional bistatic MIMO radar. In this paper, a new unitary ESPRIT scheme for joint estimation of the direction of departure (DOD) and the direction of arrival (DOA) for MOTS MIMO radar is proposed. In our method, each overlapped-transmit-subarray (OTS) with the identical effective aperture is regarded as a transmit element and the characteristics that the phase delays between the two OTSs is utilized. First, the measurements corresponding to all the OTSs are partitioned into two groups which have a rotational invariance relationship with each other. Then, the properties of centro-Hermitian matrices and real-valued rotational invariance factors are exploited to double the measurement samples and reduce computational complexity. Finally, the close-formed solution of automatically paired DOAs and DODs of targets is derived in a new manner. The proposed scheme provides increased estimation accuracy with the combination of inherent advantages of MOTS MIMO radar with unitary ESPRIT. Simulation results are presented to demonstrate the effectiveness and advantage of the proposed scheme.

摘要

多重叠发射子阵列配置的双基地多输入多输出(MOTS MIMO)雷达的发射阵列被划分为多个重叠子阵列,这与传统双基地MIMO雷达不同。本文提出了一种用于MOTS MIMO雷达联合估计出发方向(DOD)和到达方向(DOA)的新型酉ESPRIT方案。在我们的方法中,将具有相同有效孔径的每个重叠发射子阵列(OTS)视为一个发射单元,并利用两个OTS之间的相位延迟特性。首先,将与所有OTS对应的测量值划分为两组,这两组彼此具有旋转不变关系。然后,利用中心厄米特矩阵和实值旋转不变因子的性质来增加测量样本并降低计算复杂度。最后,以一种新的方式推导了目标DOA和DOD自动配对的闭式解。所提出的方案结合了MOTS MIMO雷达与酉ESPRIT的固有优势,提高了估计精度。给出了仿真结果以证明所提方案的有效性和优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef01/4178986/80385c6421a4/sensors-14-14411f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验