本文選題：次聲信號 + 特征提取��； 參考：《中國地質大學(北京)》2015年碩士論文

【摘要】：次聲是一種人耳聽不到的低頻信號,它的頻率范圍在0.01~20Hz之間。自然界中海嘯、火山噴發(fā)、極光、地震、泥石流,人類活動中的核爆炸、火箭發(fā)射、炮兵射擊等都會產生次聲信號。由于各種事件激發(fā)次聲的機理不盡相同,各類型事件產生的次聲信號在頻率軸上的能量分布不同。從監(jiān)測到的次聲信號本身的特點,可以反推出產生次聲信號的事件類型,從而達到次聲信號分類識別的目的。對次聲信號的特征提取和分類識別研究一直是次聲信號處理領域中的熱點內容�？偨Y前人的研究成果發(fā)現(xiàn),次聲信號的分類識別算法主要是沿著兩條主線路不斷的取得突破和發(fā)展。一方面是次聲信號特征提取技術的研究;另一方面是模式識別算法的設計研究。次聲事件識別的關鍵環(huán)節(jié)是前者,即如何從信號中提取有效的特征作為識別依據。而識別效果的好壞本質上也是由所選用的模式特征決定的。對特征提取這一方向的研究重點主要在于挖掘能夠表現(xiàn)信號類別的特征,以及如何有效的提取這些特征的信號處理技術。分類識別這一部分的研究重點在于合適的匹配提取到的特征向量,研究各種分類模型的算法和結構,設計準確高效的分類器,完成準確劃分信號類別的最終目的。本文將研究通過次聲信號對自然災害進行事件分類,研究的重點是次聲信號特征提取的技術方法和模式識別分類算法的設計。本論文的主要內容如下:論文首先詳細地介紹了自然災害中次聲信號分類識別的研究背景和重要意義,著重對比分析了各種次聲信號特征提取算法的優(yōu)點和不足;研究了三種技術方法用于次聲信號的特征提取和兩種分類算法用于分類識別;在此基礎上,使用采集的次聲數(shù)據對完整的分類模型進行試驗驗證。分析試驗結果,對比各個方法的有效性。本論文以自然災害中各種事件產生的次聲信號為研究對象,以降低實際次聲監(jiān)測中的誤報率為目的,研究了地震、海嘯、火山、泥石流等所產生的次聲信號的特征提取技術和分類識別算法。期望本研究的成果可以對次聲信號處理方法和實際應用產生一定的參考作用。
[Abstract]:Infrasound is a kind of low frequency signal which can not be heard by human ear. Its frequency range is between 0.01 Hz and 20 Hz. In nature, tsunamis, volcanic eruptions, auroras, earthquakes, mudslides, nuclear explosions in human activities, rocket launches and artillery fire all produce infrasound signals. The infrasound signals produced by different events have different energy distribution on the frequency axis because of the different mechanism of infrasound excitation. From the characteristics of the monitored infrasound signal, the event type of the infrasound signal can be inferred and the classification and recognition of the infrasound signal can be achieved. The feature extraction and classification recognition of infrasound signal has been a hot topic in the field of infrasound signal processing. It is found that the classification and recognition algorithm of infrasound signal is mainly a breakthrough and development along two main lines. On the one hand, the feature extraction of infrasound signal is studied; on the other hand, the design of pattern recognition algorithm is studied. The key link of infrasound event recognition is the former, that is, how to extract effective features from the signal as the basis for recognition. The recognition effect is essentially determined by the selected pattern features. The research focus of feature extraction is mainly on mining the features that can represent signal categories and how to extract these features effectively. The research focus of this part is on matching the extracted feature vectors, studying the algorithms and structures of various classification models, designing accurate and efficient classifiers, and accomplishing the final goal of accurately classifying the signals. In this paper, we will study the event classification of natural disasters by infrasound signals, focusing on the feature extraction of infrasound signals and the design of pattern recognition classification algorithm. The main contents of this paper are as follows: firstly, the research background and significance of infrasound signal classification and recognition in natural disasters are introduced in detail, and the advantages and disadvantages of various infrasound signal feature extraction algorithms are compared and analyzed. Three technical methods are studied for feature extraction of infrasound signals and two classification algorithms for classification and recognition. Based on this, the complete classification model is verified by using the collected infrasound data. The experimental results are analyzed and the effectiveness of each method is compared. In this paper, the infrasonic signals produced by various events in natural disasters are taken as the research object, with the aim of reducing the false alarm rate in the actual infrasound monitoring, the earthquake, tsunami and volcano are studied. Feature extraction technology and classification recognition algorithm of infrasound signal produced by debris flow. It is expected that the results of this study can be used as a reference for infrasound signal processing methods and practical applications.
【學位授予單位】：中國地質大學(北京)
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN912.3

【參考文獻】

相關期刊論文 前1條

1 加玉濤;羅志增;;肌電信號特征提取方法綜述[J];電子器件;2007年01期

相關博士學位論文 前1條

1 李新欣;船舶及鯨類聲信號特征提取和分類識別研究[D];哈爾濱工程大學;2012年

相關碩士學位論文 前2條

1 任際周;基于次聲波技術的滑坡監(jiān)測預警系統(tǒng)研究[D];成都理工大學;2012年

2 尚媛媛;次聲波信號分析方法研究[D];昆明理工大學;2013年

，

本文編號：2100328