發(fā)布時間：2018-06-04 19:14

  本文選題：X波段雷達(dá) + 海浪參數(shù)　； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：21世紀(jì)是人類海洋開發(fā)利用的新時代,對海洋資源的開發(fā)利用是人類經(jīng)濟(jì)進(jìn)一步發(fā)展的重要方向。海洋對于一個國家的國防安全至關(guān)重要,特別是近些年來我國海洋權(quán)益不斷遭受挑釁。同時,海洋也在不斷經(jīng)受人類的各種污染,海洋環(huán)境日益惡化。對于海洋的了解和研究一直是各種學(xué)科領(lǐng)域的熱點,特別是對海洋物理環(huán)境的監(jiān)測,關(guān)系到諸多關(guān)鍵領(lǐng)域,對于社會經(jīng)濟(jì)的發(fā)展意義重大。總之,只有更好的研究和了解海洋,海洋才能提供給我們無盡的資源。X波段雷達(dá),具有時間空間分辨率高,成本低廉,工作穩(wěn)定,安裝方便等優(yōu)點。人們使用該設(shè)備進(jìn)行海洋探測預(yù)警已有很長時間,由于其自動運(yùn)行,無需值守,能夠24小時全天候工作,X波段雷達(dá)對于浪流監(jiān)測也非常方便快捷。本論文,首先回顧了已經(jīng)成熟的幾種海面浪流參數(shù)監(jiān)測方法,以及這些年來利用X波段雷達(dá)進(jìn)行海面參數(shù)監(jiān)測的發(fā)展現(xiàn)狀。了解了雷達(dá)在海面成像以及調(diào)制原理后,初步介紹了人工神經(jīng)網(wǎng)絡(luò)的概念以及發(fā)展現(xiàn)狀。之后,針對海面表層流參數(shù)的反演,分別采用了最小二乘算法(Least Squares Method)、迭代最小二乘算法(Iterative LSM)、NSP(Normalized Scalar Product)算法�，F(xiàn)實中海浪并不是理想的線性譜疊加的關(guān)系,特別是在復(fù)雜海況下,根據(jù)線性關(guān)系得到的反演算法并不能非常精確的表征雷達(dá)圖像與海面浪流參數(shù)的關(guān)系。我們在計算有效波高時使用的調(diào)制傳遞函數(shù)為線性擬合的關(guān)系,所以計算精度有限。因此,針對有效波高,本文搭建調(diào)試一個廣義回歸神經(jīng)網(wǎng)絡(luò)來進(jìn)行海譜的擬合以及參數(shù)的反演,建立雷達(dá)圖像一維頻譜與浮標(biāo)譜之間的映射關(guān)系。海面雷達(dá)圖像的數(shù)值模擬,可以幫助我們了解后向散射信號形成機(jī)理,同時對模擬圖像的計算可以驗證三種算法的優(yōu)劣。在闡述了海浪譜的表達(dá)形式以及擬合雷達(dá)譜與模擬海譜后。針對不同算法進(jìn)行表層流以及海浪參數(shù)的模擬反演,證明了相應(yīng)算法的準(zhǔn)確可靠。利用已經(jīng)測量得到的大量海面雷達(dá)圖像數(shù)據(jù)以及對應(yīng)浮標(biāo)海流計數(shù)據(jù),本文最后進(jìn)行了實測雷達(dá)數(shù)據(jù)的參數(shù)反演,得到結(jié)果后與相對應(yīng)的浮標(biāo)或者海流計數(shù)據(jù)進(jìn)行對比分析。針對有效波高的反演,分別采用傳統(tǒng)線性擬合參數(shù)定標(biāo)的算法和廣義回歸神經(jīng)網(wǎng)絡(luò)的算法進(jìn)行橫向?qū)Ρ�。�?jīng)計算分析,各種算法對于相應(yīng)的海面浪流參數(shù)反演結(jié)果都比較好,有效波高的計算結(jié)果表明,神經(jīng)網(wǎng)絡(luò)算法能夠取得更好的結(jié)果。對比浮標(biāo)和海流計參數(shù),各參數(shù)計算結(jié)果相關(guān)系數(shù)均在0.75之上。
[Abstract]:The 21st century is a new era for the exploitation and utilization of human ocean. The exploitation and utilization of marine resources is an important direction for the further development of human economy. The ocean is very important for the national defense security of a country, especially in recent years, the maritime rights and interests of our country have been constantly challenged. At the same time, the ocean is also suffering from various kinds of human pollution, the marine environment is deteriorating day by day. The understanding and research of the ocean has always been a hot spot in various disciplines, especially the monitoring of the marine physical environment, which relates to many key fields and is of great significance to the development of social economy. In a word, only by better studying and understanding of the ocean can the ocean provide us with endless resources. X-band radar has the advantages of high spatial and temporal resolution, low cost, stable work and convenient installation. It has been used for a long time for ocean detection and early warning. Because of its automatic operation and no need to be on duty, X-band radar can work 24 hours a day. It is also very convenient and quick to monitor waves and currents. In this paper, we first review several methods of ocean surface current parameters monitoring, and the development status of using X-band radar to monitor sea surface parameters in recent years. After understanding the principle of radar imaging and modulation at sea level, the concept and development of artificial neural network (Ann) are introduced. After that, the least square algorithm and iterative least square algorithm are used to retrieve the surface current parameters, respectively, and the iterative least square algorithm (LSM) is used to calculate the NSPN Normalized Scalar Product (Normalized Scalar Product) algorithm. In reality, ocean wave is not an ideal linear spectrum superposition relation, especially in complex sea conditions, the inversion algorithm based on linear relationship can not represent the relationship between radar image and sea surface current parameters very accurately. The modulation transfer function used in calculating the effective wave height is linear fitting, so the calculation accuracy is limited. Therefore, aiming at the effective wave height, a generalized regression neural network is built to fit the sea spectrum and inverse the parameters, and the mapping relationship between the one-dimensional spectrum and the buoy spectrum of radar image is established. The numerical simulation of sea surface radar image can help us to understand the formation mechanism of backscattering signal. At the same time, the calculation of simulated image can verify the merits and demerits of the three algorithms. The expression of wave spectrum and the fitting of radar spectrum and simulated sea spectrum are described. The simulation and inversion of surface current and ocean wave parameters for different algorithms prove the accuracy and reliability of the corresponding algorithm. Using a large number of sea surface radar image data and the corresponding buoy current data, the parameter inversion of the measured radar data is carried out at the end of this paper, and the results are compared with the corresponding buoy or current meter data. For the inversion of effective wave height, the traditional linear fitting parameter calibration algorithm and the generalized regression neural network algorithm are used for lateral comparison. The results of calculation and analysis show that various algorithms are better for the inversion of sea surface current parameters. The calculation results of effective wave height show that the neural network algorithm can obtain better results. Compared with the parameters of buoy and current meter, the correlation coefficient of each parameter is above 0.75.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.52

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本文編號：1978539