本文選題：激光雷達(dá) + 氣溶膠�。� 參考：《北方民族大學(xué)》2017年碩士論文

【摘要】：本文主要研究Mie散射激光雷達(dá)信號消噪和反演氣溶膠粒子譜分布。由于Mie散射激光雷達(dá)系統(tǒng)現(xiàn)有的缺陷,使得所測得的回波信號信噪比不高,嚴(yán)重影響對消光系數(shù)、水汽混合比和偏振的研究。另外,由于氣溶膠光學(xué)厚度和粒子譜的關(guān)系式屬于第一類Fredholm積分方程,求解出的氣溶膠粒子譜rn)(通常是病態(tài)、不唯一的。為了解決這兩個問題,本文主要用小波方法對激光雷達(dá)信號消噪和研究氣溶膠粒子譜分布�；夭ㄐ盘栂胨惴ǖ难芯恐饕怯米赃m應(yīng)BP小波神經(jīng)網(wǎng)絡(luò)消噪算法來實(shí)現(xiàn)。用正交小波函數(shù)作為隱含層結(jié)點(diǎn)函數(shù),搜索算法選取網(wǎng)絡(luò)中最優(yōu)的參數(shù)、閾值和隱含層結(jié)點(diǎn)個數(shù),下降速度最快的Levenberg-Marquardt算法作為自適應(yīng)小波神經(jīng)網(wǎng)絡(luò)梯度算法。通過對比擬合輸出的均方誤差和給定的均方誤差,來調(diào)節(jié)整個BP小波神經(jīng)網(wǎng)絡(luò),直到參數(shù)最優(yōu)為止。與其它小波神經(jīng)網(wǎng)絡(luò)不同的是,本文的BP小波神經(jīng)網(wǎng)絡(luò)隱含層結(jié)點(diǎn)個數(shù)并不是給定的,而是通過搜索算法獲得的。這種方法構(gòu)造出的自適應(yīng)BP小波神經(jīng)網(wǎng)絡(luò)可隨外界環(huán)境的改變而進(jìn)行自適應(yīng)的調(diào)整。在氣溶膠粒子譜反演算法的研究中,首先假設(shè)粒子是球形的,通過Mie理論求出消光系數(shù)和散射系數(shù);其次,用CE-318測得所需的光學(xué)厚度;最后用小波Galerkin與Tikhonov正則化相結(jié)合的算法來求解。與傳統(tǒng)的迭代算法相比,本文提出的方法方便簡潔、易于計(jì)算,即用小波Galerkin方法把光學(xué)厚度和氣溶膠粒子譜的關(guān)系式離散成線性方程組的形式,再用Tikhonov正則化法求出正則解。這樣便克服了方程解的不穩(wěn)定、不唯一的缺點(diǎn)。為了驗(yàn)證兩種方法的可行性,前者與小波閾值消噪算法進(jìn)行對比,后者選取2012年11月-2016年10月銀川地區(qū)這四年中典型天氣的光學(xué)厚度數(shù)據(jù)反演氣溶膠粒子譜,并與實(shí)際的天氣狀況對比分析。實(shí)驗(yàn)結(jié)果表明:自適應(yīng)BP小波神經(jīng)網(wǎng)絡(luò)消噪方法優(yōu)于小波閾值消噪方法,并且用小波Galerkin方法反演氣溶膠粒子譜分布來分析的銀川地區(qū)天氣狀況與當(dāng)?shù)貧庀缶痔峁┑臍v史數(shù)據(jù)相吻合。
[Abstract]:In this paper, the de-noising of Mie scattering lidar signal and the inversion of aerosol particle spectrum distribution are studied. Due to the defects of Mie scattering lidar system, the signal to noise ratio (SNR) of the measured echo signal is not high, which seriously affects the study of extinction coefficient, water vapor mixing ratio and polarization. In addition, because the relation between aerosol optical thickness and particle spectrum belongs to the first kind Fredholm integral equation, the aerosol particle spectrum rn) (is usually ill-conditioned and not unique. In order to solve these two problems, the wavelet method is mainly used to de-noising the laser radar signal and to study the aerosol particle spectrum distribution. The research of echo signal de-noising algorithm is mainly realized by adaptive BP wavelet neural network de-noising algorithm. The orthogonal wavelet function is used as the hidden layer node function, and the optimal parameters, threshold and the number of hidden layer nodes in the network are selected by the search algorithm. Levenberg-Marquardt algorithm, which has the fastest descent speed, is used as the adaptive wavelet neural network gradient algorithm. The whole BP wavelet neural network is adjusted by comparing the mean square error of fitting output with the given mean square error until the parameters are optimal. Different from other wavelet neural networks, the number of hidden layer nodes in BP wavelet neural network is not given, but is obtained by searching algorithm. The adaptive BP wavelet neural network constructed by this method can be adjusted adaptively with the change of the external environment. In the research of aerosol particle spectrum inversion algorithm, the extinction coefficient and scattering coefficient are calculated by Mie theory, and the required optical thickness is measured by CE-318. Finally, wavelet Galerkin and Tikhonov regularization algorithm are used to solve the problem. Compared with the traditional iterative algorithm, the method presented in this paper is simple and convenient to calculate. The relation between optical thickness and aerosol particle spectrum is discretized into linear equations by wavelet Galerkin method, and the regular solution is obtained by Tikhonov regularization method. In this way, it overcomes the instability and ununiqueness of the solution of the equation. In order to verify the feasibility of the two methods, the former is compared with the wavelet threshold denoising algorithm, and the latter selects the optical thickness data of typical weather in Yinchuan area from November 2012 to October 2016 to retrieve aerosol particle spectrum. And compared with the actual weather conditions. The experimental results show that the adaptive BP wavelet neural network denoising method is better than the wavelet threshold denoising method. The analysis of the aerosol particle spectrum distribution using the wavelet Galerkin method coincides with the historical data provided by the local meteorological bureau.
【學(xué)位授予單位】：北方民族大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.51

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本文編號：2089093