Method based on neural network for LFMCW radar system object range superresolution estimation

被引：0

作者：

Li, Y. ^{[1
]}

Huang, J.Y. ^{[1
]}

Feng, Z.H. ^{[1
]}

机构：

[1] Natl. Lab. of Microwave, Dep. of Electr. Eng., Tsinghua Univ., Beijing 100084, China

来源：

Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves | 2000年 / 19卷 / 06期

关键词：

Computer simulation - Frequency modulation - Iterative methods - Neural networks - Signal processing;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

To replace the traditional FFT method and realize the object range super-resolution estimation, an artificial neural network method was proposed to decompose signal's auto-correlation matrix into the summation of rank-1 matrices and convert the decomposition problem to an iterative one by using Hopfield neural network. The property of this method was investigated both theoretically and experimentally. The results were compared with five other typical super-resolution algorithms including MUSIC, etc.. It is found that the present method has a lower SNR threshold and higher range resolution.

引用

页码：425 / 429