基于机场感知的航空噪声监测与分析

发布时间：2018-05-12 18:44

本文选题：机场噪声 + 噪声滤波　；参考：《南京航空航天大学》2017年硕士论文

【摘要】：伴随着我国民航业的飞速发展,机场噪声问题也日益引起社会的广泛关注。开展机场噪声的监测、预测与评估等研究,为机场噪声环境综合治理和城市的可持续发展提供有力的技术支撑,具有十分重要的意义。本文在基于机场感知的噪声监测系统的基础上,针对密集监测点测得的实时噪声数据开展分析研究,提出了实时的机场噪声滤波算法和航空噪声识别算法。本文具体研究内容如下:首先,基于粒子滤波算法,针对密集监测点之间噪声数据的相似性研究了实时的机场噪声滤波算法。对于受机场噪声影响的特定环境,研究了噪声序列的非线性模型和噪声状态的递推更新,并根据噪声序列的短时趋势以及多个监测点噪声序列之间的相似性,计算当前时刻监测点受局部噪声影响的可能性大小,得到对应时刻测得的噪声数据的可靠性,以此自适应地更新噪声序列的状态转移方程。同时,基于监测点噪声数据的可靠性,实现了机场噪声数据的实时滤波算法。实验结果显示,该算法可以有效地去除局部噪声对监测点噪声数据的影响,但保留航空噪声的影响。其次,针对密集监测点噪声数据在空间上的分布特性,研究了实时的航空噪声识别算法。提出了监测区域内各处噪声衰减趋势面的度量方法,并以此计算整个监测区域在某处的噪声影响能力;采用最优化方法,快速寻找理论噪声源的位置,最大化其噪声影响能力,以此识别航空噪声的存在。实验结果显示,上述算法可以较好的识别航空噪声的存在,同时还能够估计航空噪声源的位置及其影响范围,较好地可视化飞机的航迹和影响区域。本文的工作面向实际问题,研究基于密集监测点噪声数据的实时滤波与航空噪声识别算法,对于我国航空噪声监测技术的发展起到较大的推动作用。
[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.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB53;X839.1

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