Localization Performance Analysis of FDA Radar Receiver With Two-Stage Estimator
Localization Performance Analysis of FDA Radar Receiver With Two-Stage Estimator
复制标题
使用两级估计器的 FDA 雷达接收机定位性能分析
DOI：
10.1109/taes.2018.2831818
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发表时间：
2018-04
影响因子：
4.4
通讯作者：
Wen Wu
中科院分区：
文献类型：
--
作者：
Can Cui;Jie Xiong;Wen-Qin Wang;Wen Wu
频率分集天线阵（FDA）受到了广泛的关注，但有关FDA接收机结构和接收机处理算法的研究报道很少。在本文中，我们分析了FDA雷达定位性能的两阶段估计。讨论了三种FDA接收结构，并与相控阵（PA）进行了比较。为了克服文献中采用的单色FDA雷达信号模型忽略了FDA雷达相干发射增益的缺点，本文建立了一个包含非重叠和重叠频谱的一般FDA雷达接收信号的带宽模型。通过Cramér-Rao界（CRB）和接收信噪比来评估定位性能。该模型提供了更准确的FDA雷达接收信号的表示和更合理的FDA和PA雷达之间的比较。此外，提出了一种快速两阶段估计器，以降低二维多信号分类的计算复杂度。理论分析和数值结果均验证了所提出的方法的正确性。
Frequency diverse array (FDA) has received much attention, but little work about FDA receiver architectures and receiver processing algorithms have been reported. In this paper, we analyze the localization performance of FDA radar with a two-stage estimator. Three FDA receive architectures are discussed and compared with the phased-array (PA). In order to overcome disadvantages of the monochromatic FDA radar signal model adopted in the literature that ignores the coherent transmit gain in FDA radar, we formulate a bandwidth model for a general FDA radar received signals including both nonoverlapping and overlapping spectra. The localization performance are assessed by the Cramér–Rao bound (CRB) and receive signal noise ratio. The proposed model provides more accurate representation of FDA radar received signals and more reasonable comparisons between FDA and PA radars. Additionally, a fast two-stage estimator is proposed to reduce the two-dimensional multiple signal classification computation complexity. All proposed methods are verified by both theoretical analysis and numerical results.
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影响因子：
1.5
作者：
Wen-qin Wang;H. Shao;Jingye Cai
通讯作者：
Wen-qin Wang;H. Shao;Jingye Cai
影响因子：
5.4
作者：
R. Gui;Wen-qin Wang;Can Cui;H. So
通讯作者：
R. Gui;Wen-qin Wang;Can Cui;H. So
影响因子：
14.9
作者：
Jian Li;P. Stoica
通讯作者：
Jian Li;P. Stoica
DOI：
10.1109/maes.2009.5344178
发表时间：
2009-12
期刊：
2008 IEEE Radar Conference
影响因子：
--
作者：
J. Chazelas;A. Ziaei;D. Dolfi;T. Merlet
通讯作者：
J. Chazelas;A. Ziaei;D. Dolfi;T. Merlet
影响因子：
1.7
作者：
Jianxin Wu;Tong Wang;Xiangdong Meng;Z. Bao
通讯作者：
Jianxin Wu;Tong Wang;Xiangdong Meng;Z. Bao