本文關(guān)鍵詞：LFMCW雷達(dá)人體目標(biāo)探測與特征識別研究　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 人體探測 相位補(bǔ)償算法 時(shí)頻分析 特征提取 運(yùn)動識別 微多普勒

【摘要】：由于人體目標(biāo)雷達(dá)散射截面積較小,運(yùn)動速度緩慢,其多普勒頻率與地物雜波重疊,在強(qiáng)地物雜波環(huán)境中對慢速人體目標(biāo)探測與識別一直是雷達(dá)面臨的嚴(yán)峻挑戰(zhàn)。本文采用線性調(diào)頻連續(xù)波體制雷達(dá)針對人體探測與特征識別課題開展如下研究:首先,簡化Boulic模型為12關(guān)節(jié)、10散射部分的人體運(yùn)動模型,在人體坐標(biāo)系下完善人體12關(guān)節(jié)旋轉(zhuǎn)角度公式,10散射中心點(diǎn)位移公式,基于此構(gòu)建LFMCW雷達(dá)人體運(yùn)動回波模型。通過對比實(shí)測數(shù)據(jù)與仿真模型的時(shí)頻譜圖,驗(yàn)證LFMCW雷達(dá)人體運(yùn)動回波模型的正確性。其次,針對LFMCW雷達(dá)采用長時(shí)間相干積累探測運(yùn)動人體目標(biāo)存在多普勒擴(kuò)展問題,設(shè)計(jì)基于相位補(bǔ)償變換的相干積累方法,該算法通過循環(huán)搜索尋找最佳人體運(yùn)動參數(shù),據(jù)此構(gòu)建相位補(bǔ)償信號匹配行進(jìn)人體差拍信號中的非線性相位,在相干積累時(shí)有效利用多普勒擴(kuò)散能量,提高檢測信噪比,改善LFMCW雷達(dá)對人體目標(biāo)的探測能力。仿真與實(shí)測數(shù)據(jù)分析結(jié)果驗(yàn)證了該算法的有效性。最后,針對LFMCW雷達(dá)人體運(yùn)動識別問題,在時(shí)頻域分析人體運(yùn)動模型與實(shí)測數(shù)據(jù)在多種運(yùn)動形式下的微多普勒變化規(guī)律,歸納人體運(yùn)動物理特征值,據(jù)此設(shè)計(jì)支持向量機(jī)分類樹結(jié)構(gòu)的人體運(yùn)動形式分類器。在大量實(shí)測數(shù)據(jù)的基礎(chǔ)上,分析分類器采用不同人體運(yùn)動特征值時(shí)的識別性能。
[Abstract]:Because the human body target RCS is smaller, speed is slow, the Doppler frequency and the ground clutter overlap in strong ground clutter environment to slow human target detection and recognition has been a severe challenge for radar. This paper adopts linear frequency modulated continuous wave radar system for human detection and recognition task to carry out the research as follows: first, a simplified Boulic model for 12 joint human motion model 10 scattering part of the rotation angle of the human body 12 joint formula in the body coordinate system perfect, 10 scattering center displacement formula, the construction of LFMCW radar echo model based on human motion. The spectrum of the measured data and simulation model, the correctness of model validation LFMCW the radar echo of human motion. Secondly, according to the LFMCW radar using long time coherent detection of moving human target Doppler expansion problem, based on the design phase A method of coherent accumulation compensation transformation, the algorithm iteratively searches for the best human motion parameters, based on phase compensation signal, pedestrian beat nonlinear phase signal, the effective use of Doppler diffusion energy in coherent accumulation, improve the signal-to-noise ratio and improve the detection ability of LFMCW radar for human target. The simulation and measurement the data analysis results show the effectiveness of the algorithm. Finally, LFMCW radar for human motion recognition problems in time-frequency analysis of human motion model and the measured data in a variety of sports in the form of micro Doppler changes, summed up the value of physical characteristic of human motion, based on support vector machine classifier form human motion classification tree structure in. Based on the measured data, analysis of different classifiers using human motion recognition performance value.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.51

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本文編號：1441309