A Study on Radar Target Detection Based on Deep Neural Networks

被引：11

作者：

Wang, Li ^{[1
]}

Tang, Jun ^{[1
]}

Liao, Qingmin ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

来源：

IEEE SENSORS LETTERS | 2019年 / 3卷 / 03期

关键词：

Sensor signals processing; convolutional neural network (CNN); deep learning; radar processing; target detector; CLASSIFICATION;

D O I：

10.1109/LSENS.2019.2896072

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：

Target detection is one of the most important radar applications widely used in practice. Target detection can be regarded as a kind of classification, which distinguishes whether the signal undertested consists of an echo from a target (target present) or just corresponds to the noise (target absent). The deep neural network (DNN) is a popular topic for classification and has successfully been applied in different fields of science. Recently, many researchers have proposed DNNs for radar applications. In this article, we analyze a possible application of DNN- for target detection in radar, DNN-based detectors are designed, and the performance of the detector is demonstrated by comparison with traditional target detectors.

引用

页数：4

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