基于EMD和BP神经网络的雷达体征信号检测算法

Radar Vital Sign Detection Method Based on the EMD and BP Algorithm

随着城市生活中医疗、治安、反恐等方面的需求日益突出,非接触式雷达生命体征检测逐渐得到各方面的关注.文章提出一种基于EMD和神经网络的雷达生命体征信号检测算法.由于UWB雷达回波信号的非平稳非线性特性,利用EMD的空间时间尺度特性对信号进行分解,得到一系列的本征模态函数IMF,然后通过结合了免疫遗传算法IGA的BP神经网络对信号进行优化,获得心跳和呼吸信号.结果表明,文章提出的算法比直接用EMD分解重构的信号的准确性高,弥补了EMD分解的端点效应问题,具有广阔的应用前景和研究价值.

With the increasing demand for medical treatment, public security, anti-terrorism and other aspects of urban life, the vital signs detection of non-contact radar is gradually getting the attention. In this paper, an algorithm for radar vital signs detection based on EMD and neural networks is presented. Due to the non-linear and non-stationary characteristics of UWB radar echo signal, this paper utilizes the space and time scales characteristics of the EMD to decompose the signal and obtain a series of IMF. By combining the BP and IGA neural networks, it optimizes the signal and obtains the heart and respiratory signals. The experimental results show that the proposed algorithm is more accurate than the direct EMD decomposition and reconstruction of the signal, which makes up for the end effect of EMD decomposition, and has broad application prospects and research value.