本文選題：超寬帶　切入點：人體姿勢識別　出處：《北京郵電大學》2015年碩士論文　論文類型：學位論文

【摘要】：超寬帶(Ultra-wideband, UWB)技術具有多徑分辨能力強、穿透能力強以及功耗低的特點,廣泛應用于障礙物檢測以及目標識別等領域。同時,伴隨著人機交互需求的發(fā)展,關于人體姿勢識別的研究越來越多。本文結合機器學習的理論,提出了挖掘UWB信號傳感信息來識別人體姿勢的方法。針對基于支持向量機(Support Vector Machine, SVM)的人體姿勢識別、改進的混沌自適應遺傳算法以及人體姿勢識別驗證平臺三個方面展開了研究,主要工作如下：針對基于SVM的人體姿勢識別問題,為了有效提取8種動作UWB信號的傳感信息,重點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頻率成分,計算每個頻段的能量和,最后得到歸一化的小波包能量分布特征。結果表明,小波包能量特征具有良好的可分性,能夠顯著提高姿勢識別的準確率。針對SVM的參數(shù)對識別性能影響較大的問題,利用改進的混沌自適應遺傳算法對SVM參數(shù)進行了優(yōu)化研究。考慮到標準遺傳算法中交叉和變異概率需要預先確定且在算法中維持不變,當整個種群適應度比較接近時,進化將會變慢。本文提出了改進的混沌自適應遺傳算法(Improved Chaos Adaptive Genetic Algorithm, ICAGA),它采用動態(tài)的交叉和變異概率,且對種群中具有最高適應度的個體進行給定步數(shù)的混沌優(yōu)化搜索,從而指導整個群體向最優(yōu)解方向進化,改進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。將改進的遺傳算法優(yōu)化應用于人體姿勢識別,結果表明,改進算法可以提高SVM的參數(shù)尋優(yōu)速度。在上述分析研究成果的基礎上,結合MATLAB的GUI仿真平臺,設計開發(fā)了基于UWB與SVM的人體姿勢識別驗證平臺,實現(xiàn)了對人體姿勢的識別。論文最后對全文工作進行了總結,并對人體姿勢識別的相關研究提出了展望。
[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.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN925;TP181

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