频域积累与EMD去噪结合的微动信号检测算法

 超宽带生命探测雷达具有穿透能力强、距离分辨率高、抗干扰能力强等诸多优点，在防暴、救援、反恐等领域有很高的使用价值。由于穿墙生命探测雷达回波信号具有杂波干扰严重，且生命信号与背景噪声相互交叠等特点，利用传统数字滤波方法无法有效检测人体微动信号。针对此问题，本文提出一种利用频域积累与经验模态分解（EMD）相结合的人体微动信号检测算法，利用频域积累提高回波信号的信噪比，利用EMD 方法进行进一步的去噪处理。该算法不仅具有频域积累可以有效提高信噪比的优点，而且具有EMD 方法自适应分解信号的能力。同时，它克服了在低信噪比情况下，频域积累实时性不强，以及EMD 方法不能有效去除杂波的缺点。仿真和实验证明，该算法既可以有效提高雷达回波信号的信噪比，又可以改善单纯使用频域积累实时性不强的缺点，利用该算法对雷达回波信号进行处理可以准确快速地检测出人体的呼吸频率，是一种很好的穿墙雷达微弱信号检测新方法。

Micro-tremor Signal Detection Algorithm Based on Frequency Domain Cumulation Combined with EMD De-noising

Ultra-wideband (UWB) life detection radar has found wide applications in anti-riot, rescue, and anti-terrorism actions due to its many advantages, such as strong penetrating ability, high distance resolution, and strong anti-interference ability. Therefore, research on human micro-tremor signal detection using UWB through-wall radar has much significance. As the echo signal of through-wall life detection radar is interfered by background noise clutter, the conventional method using the digital filter cannot effectively detect human micro-tremor signal. To solve this problem, this paper presents a human body micro-tremor signal detection algorithm. First, the frequency domain cumulation is used to increase the signal-to-noise ratio (SNR) of the echo signal, and then EMD is used for de-noising. This algorithm not only has the advantages of frequency domain cumulation, but also has the ability of EMD method for adaptive decomposition. Under low SNR, it overcomes the disadvantages of frequency domain cumulation and EMD method, the former being not real-time and the latter having ineffective clutter remove. The simulation results show that the algorithm not only improves the SNR of the radar echo signal, but also overcomes the disadvantage of frequency domain cumulation. The frequency of human respiration can be rapidly and accurately detected by processing the echo signal using this algorithm, which is a novel method for through-wall radar detection of weak signals.