EEMD在超宽带雷达生命信号提取中的应用

超宽带生命探测雷达回波信号具有非线性、非平稳特性,由于心跳信号能量较弱,且受到呼吸谐波的干扰,传统的数字滤波方法无法有效地提取心跳信号。鉴于此采用一种从时域上提取生命信号的新方法。应用聚类经验模式分解(EEMD)将生命信号分解成有限个固有模态函数(IMF),再依据模式判别准则从时域上重构呼吸和心跳信号。实验结果表明,相比于经验模式分解(EMD),EEMD能有效提取呼吸信号和心跳信号。

Life detection and location methods using UWB impulse radar in a coal mine

An ultra-wideband impulse radar was studied for the detection of buried life in coal mines. An improved Empirical Mode Decomposition (EMD) method based on a cross-correlation filter was proposed for reduction of multipath and noise interference. Multipath interference was first removed by cross-corre- lation filtering. Then the delays of each pulse in every echo were summed. An EMD algorithm was used for noise reduction for the total delay of each echo. The corresponding EMD results of every echo were then summed and averaged. Finally, evidence for the existence of buried life and their position were obtained from amplitude-frequency curves of the averaged EMD results. Detailed simulation experi- ments are presented to validate the effectiveness of this proposed method. The experimental results show that this method can efficiently eliminate multipath interference and reduce noise interference in echoes, which makes detection and location of buried life in coal mines more accurate.

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。