【摘要】：基于微波雷達(dá)的人體探測(cè)主要是利用人體運(yùn)動(dòng)形成的多普勒效應(yīng),實(shí)現(xiàn)對(duì)人體的檢測(cè)、定位和跟蹤,在現(xiàn)代城市戰(zhàn)爭(zhēng)、自然災(zāi)害救援、醫(yī)療監(jiān)護(hù)等領(lǐng)域具有重要的應(yīng)用價(jià)值。由于實(shí)際探測(cè)中場(chǎng)景復(fù)雜,人體微攝動(dòng)以及步態(tài)信號(hào)回波形式多樣、能量微弱,造成難以有效檢測(cè)。本文圍繞穿墻場(chǎng)景下人體檢測(cè)難題開(kāi)展研究,主要工作和貢獻(xiàn)如下:1.針對(duì)穿墻場(chǎng)景下的人體探測(cè)難題,研究了單頻連續(xù)波雷達(dá)體制和步進(jìn)頻連續(xù)波雷達(dá)體制下的人體回波信號(hào)模型,并對(duì)人體回波信號(hào)進(jìn)行分析。通過(guò)仿真確定了穿墻人體探測(cè)情況下雷達(dá)發(fā)射信號(hào)的最佳頻段;2.針對(duì)低信雜比、復(fù)雜非高斯雜波場(chǎng)景下人體微攝動(dòng)信號(hào)穩(wěn)健檢測(cè)難題,提出了雙通道相關(guān)熵探測(cè)算法,該算法首先通過(guò)計(jì)算回波信號(hào)的相關(guān)熵,實(shí)現(xiàn)對(duì)雜波的抑制以及人體微攝動(dòng)信號(hào)能量的積累,然后對(duì)相關(guān)熵做傅里葉變換,得到相關(guān)熵譜,從而實(shí)現(xiàn)人體探測(cè)。通過(guò)與傳統(tǒng)人體探測(cè)算法效果的對(duì)比,證明了該算法能夠有效提高探測(cè)結(jié)果信噪比;3.針對(duì)人體微攝動(dòng)信號(hào)回波能量弱,單幅回波圖像包含信息量少的情況,研究了利用雙幅圖像進(jìn)行相關(guān)分析的人體探測(cè)算法,該算法通過(guò)目標(biāo)與場(chǎng)景的相關(guān)分析,有效加強(qiáng)了微弱目標(biāo)信號(hào)能量,有利于從強(qiáng)雜波中提取人體微弱信息。通過(guò)仿真驗(yàn)證了該算法的有效性,并與傳統(tǒng)人體探測(cè)算法進(jìn)行了效果上的對(duì)比;4.針對(duì)人體步態(tài)回波信號(hào)具有時(shí)變性強(qiáng),包含頻率分量多的情況,將時(shí)頻分析方法用于人體步態(tài)回波信號(hào)分析,首先對(duì)人體步態(tài)回波進(jìn)行短時(shí)傅里葉變換,得到人體步態(tài)運(yùn)動(dòng)回波時(shí)頻圖,然后對(duì)二值化處理,提取上下包絡(luò),最后對(duì)譜圖中的不同參數(shù)進(jìn)行分析,并對(duì)從譜圖中提取步態(tài)特征參數(shù)的方法進(jìn)行了總結(jié);5.針對(duì)運(yùn)動(dòng)狀態(tài)的人體回波形式多樣的情況,進(jìn)行了將支持向量機(jī)分類方法用于人體運(yùn)動(dòng)狀態(tài)識(shí)別領(lǐng)域的研究。將人體步態(tài)譜圖中代表不同物理意義的特征參數(shù)值作為支持向量機(jī)的輸入,從而實(shí)現(xiàn)人體不同運(yùn)動(dòng)狀態(tài)的分類。
[Abstract]:The human body detection based on the microwave radar is mainly the Doppler effect formed by the movement of the human body, realizes the detection, positioning and tracking of the human body, and has important application value in the fields of modern city war, natural disaster rescue, medical monitoring and the like. Due to the complex scene, the micro-perturbation of the human body and the various forms of the gait signal echo, the energy is weak, which makes it difficult to detect effectively. This paper studies the problem of human body detection under the wall-wall scene, and the main work and contribution are as follows:1. The human body echo signal model under the system of single-frequency continuous wave radar and the step-frequency continuous wave radar system is studied in the light of the human body detection problem under the through-wall scene, and the human body echo signal is analyzed. The best frequency range of radar emission signal under the condition of through-wall human detection is determined by simulation. in ord to solve that problem of robust detection of the micro-perturbation signal in a complex non-Gaussian clutter scene for low signal-to-noise ratio and complex non-Gaussian clutter, a two-channel correlation entropy detection algorithm is proposed, which first achieves the suppression of clutter and the accumulation of the energy of the micro-perturbation signal of the human body by calculating the relative entropy of the echo signal, Then carrying out Fourier transform on the related entropy to obtain the relevant entropy spectrum, so as to realize human body detection. Compared with the traditional human body detection algorithm, it is proved that the algorithm can effectively improve the signal-to-noise ratio of the detection result. Aiming at the weak echo energy of the micro-perturbation signal of the human body, the single-amplitude echo image contains a small amount of information, the human body detection algorithm using the double-amplitude image to carry out correlation analysis is researched, the algorithm can effectively enhance the energy of the weak target signal through the correlation analysis of the target and the scene, And the weak information of the human body can be extracted from the strong clutter. The effectiveness of the algorithm is verified by simulation, and compared with the traditional human body detection algorithm. the time-frequency analysis method is used for analyzing the gait echo signals of a human body, The upper and lower envelope is extracted, and the different parameters in the spectrum are analyzed, and the method for extracting the gait characteristic parameters from the spectrogram is summarized. In this paper, the support vector machine classification method is applied to the field of human motion state identification. And the characteristic parameter values representing different physical meanings in the human gait spectrum map are used as input of the support vector machine so as to realize the classification of different motion states of the human body.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.51

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本文編號(hào)：2501151