本文選題：機(jī)場(chǎng)噪聲 + 噪聲濾波　； 參考：《南京航空航天大學(xué)》2017年碩士論文

【摘要】：伴隨著我國(guó)民航業(yè)的飛速發(fā)展,機(jī)場(chǎng)噪聲問(wèn)題也日益引起社會(huì)的廣泛關(guān)注。開(kāi)展機(jī)場(chǎng)噪聲的監(jiān)測(cè)、預(yù)測(cè)與評(píng)估等研究,為機(jī)場(chǎng)噪聲環(huán)境綜合治理和城市的可持續(xù)發(fā)展提供有力的技術(shù)支撐,具有十分重要的意義。本文在基于機(jī)場(chǎng)感知的噪聲監(jiān)測(cè)系統(tǒng)的基礎(chǔ)上,針對(duì)密集監(jiān)測(cè)點(diǎn)測(cè)得的實(shí)時(shí)噪聲數(shù)據(jù)開(kāi)展分析研究,提出了實(shí)時(shí)的機(jī)場(chǎng)噪聲濾波算法和航空噪聲識(shí)別算法。本文具體研究?jī)?nèi)容如下:首先,基于粒子濾波算法,針對(duì)密集監(jiān)測(cè)點(diǎn)之間噪聲數(shù)據(jù)的相似性研究了實(shí)時(shí)的機(jī)場(chǎng)噪聲濾波算法。對(duì)于受機(jī)場(chǎng)噪聲影響的特定環(huán)境,研究了噪聲序列的非線性模型和噪聲狀態(tài)的遞推更新,并根據(jù)噪聲序列的短時(shí)趨勢(shì)以及多個(gè)監(jiān)測(cè)點(diǎn)噪聲序列之間的相似性,計(jì)算當(dāng)前時(shí)刻監(jiān)測(cè)點(diǎn)受局部噪聲影響的可能性大小,得到對(duì)應(yīng)時(shí)刻測(cè)得的噪聲數(shù)據(jù)的可靠性,以此自適應(yīng)地更新噪聲序列的狀態(tài)轉(zhuǎn)移方程。同時(shí),基于監(jiān)測(cè)點(diǎn)噪聲數(shù)據(jù)的可靠性,實(shí)現(xiàn)了機(jī)場(chǎng)噪聲數(shù)據(jù)的實(shí)時(shí)濾波算法。實(shí)驗(yàn)結(jié)果顯示,該算法可以有效地去除局部噪聲對(duì)監(jiān)測(cè)點(diǎn)噪聲數(shù)據(jù)的影響,但保留航空噪聲的影響。其次,針對(duì)密集監(jiān)測(cè)點(diǎn)噪聲數(shù)據(jù)在空間上的分布特性,研究了實(shí)時(shí)的航空噪聲識(shí)別算法。提出了監(jiān)測(cè)區(qū)域內(nèi)各處噪聲衰減趨勢(shì)面的度量方法,并以此計(jì)算整個(gè)監(jiān)測(cè)區(qū)域在某處的噪聲影響能力;采用最優(yōu)化方法,快速尋找理論噪聲源的位置,最大化其噪聲影響能力,以此識(shí)別航空噪聲的存在。實(shí)驗(yàn)結(jié)果顯示,上述算法可以較好的識(shí)別航空噪聲的存在,同時(shí)還能夠估計(jì)航空噪聲源的位置及其影響范圍,較好地可視化飛機(jī)的航跡和影響區(qū)域。本文的工作面向?qū)嶋H問(wèn)題,研究基于密集監(jiān)測(cè)點(diǎn)噪聲數(shù)據(jù)的實(shí)時(shí)濾波與航空噪聲識(shí)別算法,對(duì)于我國(guó)航空噪聲監(jiān)測(cè)技術(shù)的發(fā)展起到較大的推動(dòng)作用。
[Abstract]:With the rapid development of China's civil aviation industry, the airport noise problem has attracted more and more attention. It is of great significance to carry out the research of airport noise monitoring, prediction and evaluation to provide strong technical support for the comprehensive management of airport noise environment and the sustainable development of the city. In this paper, based on the noise monitoring system based on airport perception, the real-time noise filtering algorithm and the aviation noise recognition algorithm are proposed for analyzing the real-time noise data obtained from the dense monitoring points. The main contents of this paper are as follows: firstly, based on particle filter algorithm, a real-time airport noise filtering algorithm is studied for the similarity of noise data between dense monitoring points. For the special environment affected by airport noise, the nonlinear model of noise sequence and the recursive updating of noise state are studied. According to the short term trend of noise sequence and the similarity between noise sequences of multiple monitoring points, the nonlinear model of noise sequence and the recursive updating of noise state are studied. The probability of local noise at the current monitoring point is calculated and the reliability of the noise data measured at the corresponding time is obtained so that the state transfer equation of the noise sequence can be updated adaptively. At the same time, based on the reliability of noise data of monitoring points, the real-time filtering algorithm of airport noise data is realized. Experimental results show that the algorithm can effectively remove the influence of local noise on the noise data of monitoring points, but retain the effect of aviation noise. Secondly, aiming at the spatial distribution of noise data from dense monitoring points, a real-time recognition algorithm for airborne noise is studied. In this paper, the measurement method of noise attenuation trend surface in monitoring area is proposed, and the noise influence ability of the whole monitoring area is calculated, and the optimal method is adopted to find the position of theoretical noise source quickly and maximize its noise influence ability. This is used to identify the presence of airborne noise. The experimental results show that the above algorithm can recognize the existence of aviation noise, estimate the position of the noise source and its influence range, and visualize the flight track and the affected area. In this paper, the real time filtering and recognition algorithm based on dense monitoring point noise data are studied, which plays an important role in the development of aviation noise monitoring technology in China.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TB53;X839.1

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