A UNIFIED APPROACH FOR TARGET DIRECTION FINDING BASED ON CONVOLUTIONAL NEURAL NETWORKS

被引：0
|
作者：
Wang, Chong [1 ,2 ]
Liu, Wei [2 ]
Jiang, Mengdi [2 ,3 ]
机构：
[1] Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo, Peoples R China
[2] Univ Sheffield, Dept Elect & Elect Engn, Sheffield, S Yorkshire, England
[3] Henan Univ, Sch Phys & Elect, Kaifeng, Peoples R China
来源：
PROCEEDINGS OF THE 2020 IEEE 30TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2020年
基金：
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词：
Target direction finding; CNN; thinned coprime array; multi-label classification; NESTED ARRAYS;
D O I：
10.1109/mlsp49062.2020.9231787
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A convolutional neural network (CNNs) based approach for target direction finding with the thinned coprime array (TCA) as an example is proposed. The ResNeXt network is adopted as the backbone network with a multi-label classification modification to find directions of an unknown number of targets. Unlike the traditional wisdom, where an additional co-array operation is needed for underdetermined direction finding (the number of sources is larger than the number of physical sensors), in the proposed approach, it is shown that the same network with raw data as its input can deal with both the overdetermined and underdetermined cases, although using covariance matrix of the data can reduce the complexity of the whole training process at the cost of estimation performance.
引用
收藏
页数：6
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