本文選題：非接觸式 + 時分短時窗　； 參考：《南京理工大學》2017年碩士論文

【摘要】：非接觸式生命信號檢測技術是利用雷達遠距離測量人體呼吸、心跳特征的新興技術。近年來,隨著檢測基礎理論的完善,硬件成本的降低,該技術逐漸進入實際應用階段。本文正是在這樣的背景下,針對實際應用中的家庭監(jiān)護、醫(yī)療檢測兩種熱點場景,分別研究了兩類生命信號特征提取方法:(1)面向家庭監(jiān)護應用的實時提取方法。為提高實時性,本方法利用低頻載波下的小角度近似解調,直接獲取近似生命信號。通過時域尋峰法對呼吸頻率進行實時提取。針對傳統(tǒng)方法提取心率延時較長的缺點,提出基于時分短時窗的心率快速提取算法。并通過仿真和實測驗證,該算法單次測量延時為2-5s,相比傳統(tǒng)算法實時性顯著提高。(2)面向醫(yī)療檢測應用的高精度提取方法。為提高精度,本方法在高頻載波下獲取高相位分辨率的雷達基帶信號。對實際硬件電路存在的正交失衡、直流偏置的影響,采用橢圓校準算法、擬合圓算法對精密運動信息進行恢復。提出擴展DACM正交解調算法獲取生命信號線性波形。設計單擺實驗測量了系統(tǒng)的正交失衡因子,同時驗證了基帶信號預處理算法的必要性、可靠性。采用完全集合經驗模態(tài)算法(CEEMD)對呼吸、心跳信號進行分離,經與參考值對比,呼吸、心跳速率測量準確率達到98%以上,提取出的心跳時域波形達到心搏間隔可測,為醫(yī)學診斷提供了具有參考意義的特征信息。對上述兩種應用場景,分別搭建了實時提取系統(tǒng)、高精度提取系統(tǒng),完成了系統(tǒng)的調試工作,并分析兩種系統(tǒng)在人體抖動和呼吸諧波干擾下的性能與誤差,并得出相應的結論,為后續(xù)工作提供了一定思路。
[Abstract]:Non-contact life signal detection technology is a new technology which uses radar to measure human breathing and heartbeat characteristics. In recent years, with the improvement of detection theory and the reduction of hardware cost, the technology has gradually entered the stage of practical application. Under this background, this paper studies two kinds of life signal feature extraction methods: one is the real time extraction method for the family monitoring application, the other is the real time extraction method for the family monitoring application, aiming at the two hot spots of family monitoring and medical detection. In order to improve the real-time performance, the approximate life signal is obtained by using the small angle approximate demodulation under low frequency carrier. The time domain peak finding method was used to extract the respiratory frequency in real time. Aiming at the disadvantage of the traditional method, a fast heart rate extraction algorithm based on time division short time window is proposed. The simulation and experimental results show that the time delay of single measurement is 2-5s. Compared with the traditional algorithm, the real-time performance of the proposed algorithm is significantly improved. (2) A high-precision extraction method for medical detection applications is proposed. In order to improve the accuracy, a high phase resolution radar baseband signal is obtained under high frequency carrier. For the effect of orthogonal imbalance and DC bias in the actual hardware circuit, the elliptical calibration algorithm and the fitting circle algorithm are used to restore the precise motion information. An extended DACM quadrature demodulation algorithm is proposed to obtain linear waveform of life signal. A single pendulum experiment was designed to measure the orthogonal imbalance factor of the system, and the necessity and reliability of the baseband signal preprocessing algorithm were verified. The complete set empirical mode algorithm (CEEMD) was used to separate the respiration and heartbeat signals. Compared with the reference values, the measurement accuracy of respiration and heartbeat rate was over 98%, and the extracted time-domain waveform of heartbeat could be measured. It provides the characteristic information for medical diagnosis. For the two application scenarios mentioned above, the real-time extraction system and the high-precision extraction system are built, the debugging work of the system is completed, and the performance and error of the two systems under the human body jitter and respiratory harmonic interference are analyzed, and the corresponding conclusions are drawn. It provides some ideas for the follow-up work.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.7

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