本文選題：超寬帶　切入點(diǎn)：人體姿勢(shì)識(shí)別　出處：《北京郵電大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：超寬帶(Ultra-wideband, UWB)技術(shù)具有多徑分辨能力強(qiáng)、穿透能力強(qiáng)以及功耗低的特點(diǎn),廣泛應(yīng)用于障礙物檢測(cè)以及目標(biāo)識(shí)別等領(lǐng)域。同時(shí),伴隨著人機(jī)交互需求的發(fā)展,關(guān)于人體姿勢(shì)識(shí)別的研究越來越多。本文結(jié)合機(jī)器學(xué)習(xí)的理論,提出了挖掘UWB信號(hào)傳感信息來識(shí)別人體姿勢(shì)的方法。針對(duì)基于支持向量機(jī)(Support Vector Machine, SVM)的人體姿勢(shì)識(shí)別、改進(jìn)的混沌自適應(yīng)遺傳算法以及人體姿勢(shì)識(shí)別驗(yàn)證平臺(tái)三個(gè)方面展開了研究,主要工作如下：針對(duì)基于SVM的人體姿勢(shì)識(shí)別問題,為了有效提取8種動(dòng)作UWB信號(hào)的傳感信息,重點(diǎn)分析了基于小波包分解的特征提取方法。首先,利用小波包分解求出各個(gè)頻率成分,計(jì)算每個(gè)頻段的能量和,最后得到歸一化的小波包能量分布特征。結(jié)果表明,小波包能量特征具有良好的可分性,能夠顯著提高姿勢(shì)識(shí)別的準(zhǔn)確率。針對(duì)SVM的參數(shù)對(duì)識(shí)別性能影響較大的問題,利用改進(jìn)的混沌自適應(yīng)遺傳算法對(duì)SVM參數(shù)進(jìn)行了優(yōu)化研究�？紤]到標(biāo)準(zhǔn)遺傳算法中交叉和變異概率需要預(yù)先確定且在算法中維持不變,當(dāng)整個(gè)種群適應(yīng)度比較接近時(shí),進(jìn)化將會(huì)變慢。本文提出了改進(jìn)的混沌自適應(yīng)遺傳算法(Improved Chaos Adaptive Genetic Algorithm, ICAGA),它采用動(dòng)態(tài)的交叉和變異概率,且對(duì)種群中具有最高適應(yīng)度的個(gè)體進(jìn)行給定步數(shù)的混沌優(yōu)化搜索,從而指導(dǎo)整個(gè)群體向最優(yōu)解方向進(jìn)化,改進(jìn)了遺傳算法可能陷入局部最優(yōu)解的缺陷,并加快了搜索速度。將改進(jìn)的遺傳算法優(yōu)化應(yīng)用于人體姿勢(shì)識(shí)別,結(jié)果表明,改進(jìn)算法可以提高SVM的參數(shù)尋優(yōu)速度。在上述分析研究成果的基礎(chǔ)上,結(jié)合MATLAB的GUI仿真平臺(tái),設(shè)計(jì)開發(fā)了基于UWB與SVM的人體姿勢(shì)識(shí)別驗(yàn)證平臺(tái),實(shí)現(xiàn)了對(duì)人體姿勢(shì)的識(shí)別。論文最后對(duì)全文工作進(jìn)行了總結(jié),并對(duì)人體姿勢(shì)識(shí)別的相關(guān)研究提出了展望。
[Abstract]:Ultra-Wideband Ultra-wideband (UWB) technology is widely used in obstacle detection and target recognition due to its strong multi-path resolution, strong penetration and low power consumption. There are more and more researches on human posture recognition. Based on the theory of machine learning, this paper proposes a method to mine the sensing information of UWB signal to recognize human posture. The improved chaotic adaptive genetic algorithm and the verification platform of human posture recognition are studied. The main work is as follows: aiming at the problem of human posture recognition based on SVM, in order to extract the sensing information of eight kinds of action UWB signals effectively, The feature extraction method based on wavelet packet decomposition is analyzed. Firstly, the energy sum of each frequency band is calculated by wavelet packet decomposition, and the normalized wavelet packet energy distribution is obtained. The energy feature of wavelet packet has good separability and can improve the accuracy of posture recognition significantly. Aiming at the problem that the parameters of SVM have great influence on the recognition performance, The improved chaotic adaptive genetic algorithm is used to optimize the SVM parameters. Considering that the crossover and mutation probabilities in the standard genetic algorithm need to be determined in advance and remain unchanged in the algorithm, when the population fitness is close, This paper presents an improved Chaos Adaptive Genetic algorithm (ICAGAA), which uses dynamic crossover and mutation probability, and performs chaotic optimization search for individuals with the highest fitness in the population. Thus, the whole population is guided to evolve towards the optimal solution, and the defect of the genetic algorithm which may fall into the local optimal solution is improved, and the search speed is accelerated. The improved genetic algorithm is applied to human posture recognition, and the results show that, The improved algorithm can improve the speed of parameter optimization of SVM. Based on the above research results, combined with the GUI simulation platform of MATLAB, a human posture recognition verification platform based on UWB and SVM is designed and developed. Finally, the thesis summarizes the work of the whole paper, and puts forward the prospect of the research on the recognition of human posture.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN925;TP181

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