發(fā)布時間：2018-04-09 06:26

  本文選題：匹配場處理　切入點：信息理論　出處：《浙江大學(xué)》2015年博士論文

【摘要】：本論文試圖從機器學(xué)習(xí)角度探討從水聽器測量數(shù)據(jù)中學(xué)習(xí)聲源位置信息的問題。在優(yōu)化算法指導(dǎo)下,機器學(xué)習(xí)通過最小化模型拷貝與實際測量數(shù)據(jù)之間在特定代價準(zhǔn)則下的誤差來進行數(shù)據(jù)結(jié)構(gòu)的學(xué)習(xí)。傳統(tǒng)的匹配場處理(Matched-field processing,簡稱MFP)方法(例如：Bartlett相關(guān)器,最大似然估計器和最小方差無失真響應(yīng)估計器等)是通過在聲源位置參數(shù)空間內(nèi)網(wǎng)格搜索參數(shù)并選取估計器模糊度輸出峰值處對應(yīng)的參數(shù)來作為估計值。模糊度輸出的倒數(shù)可以看做是一種廣義的誤差、網(wǎng)格搜索是一種最平白的參數(shù)搜索策略、待估計的參數(shù)反映的即是數(shù)據(jù)的結(jié)構(gòu)信息,因此說機器學(xué)習(xí)囊括了傳統(tǒng)的匹配場聲源定位方法。本論文將匹配場處理建立在機器學(xué)習(xí)框架之下,選取基于信息理論原則的代價準(zhǔn)則,來實現(xiàn)對拷貝模型與實際測量數(shù)據(jù)在信息意義下的距離測量。目前有很多信息理論方法來測量信息之間的距離,其中最常用的就是散度。本論文選取了在信息論和信號檢測中有廣泛應(yīng)用的f-散度進行重點研究。f-散度包含信息論中廣為人知的相對熵,其偶對稱形式被稱為信息散度。f-散度也包含了Hellinger積分,它可以構(gòu)成信號檢測理論中著名的錯誤概率下限Chernoff Bound。Bhattacharyya系數(shù)是Hellinger積分控制參數(shù)等于常數(shù)1/2時的結(jié)果,它可以不失一般性地表征信號檢測問題的錯誤概率上、下限,其倒數(shù)的自然對數(shù)是可以測量信息間距離的Bhattacharyya距離。在信息理論框架之內(nèi),本論文選取Bhattacharyya距離作為代價準(zhǔn)則,并仿照最小化相對熵得到最大似然估計器的方式獲得了最小化Bhattacharyya距離估計器。雖然高斯分布不能保證對測量數(shù)據(jù)的實際分布進行精確的表征,但是它通常是最合理的選擇。高斯分布具有中心極限定理、便于理論分析時的解析推導(dǎo)及易于生成其他分布的特性,使得它卓越不凡。特別的是,當(dāng)隨機過程的一、二階矩已知時,高斯分布可以最大化Cramer-Rao限。這樣,任何基于Cramer-Rao限的優(yōu)化準(zhǔn)則在高斯分布下就成為了一種Min-Max優(yōu)化準(zhǔn)則,即最小最大化的Cramer-Rao限。由于Cramer-Rao限是無偏估計可達的方差理論下限,該特性對于無偏估計方法的性能評估具有顯著的指導(dǎo)意義。本論文中,假設(shè)信號和噪聲隨機過程均服從零均值高斯分布,數(shù)據(jù)的統(tǒng)計特性可以完全由協(xié)方差矩陣表征。實際的匹配場處理過程中,受有限信號平穩(wěn)時間、相位無失真帶寬等因素影響,只能獲得有限的有效數(shù)據(jù)樣本。此時,通過最大似然估計方法獲得的采樣協(xié)方差矩陣就會因數(shù)據(jù)量有限而與數(shù)據(jù)的真實協(xié)方差矩陣存在誤差,導(dǎo)致統(tǒng)計特性失配、數(shù)據(jù)信息的失真。因此,發(fā)展能夠在統(tǒng)計特性失配情況下穩(wěn)定工作的匹配場處理方法具有重要的意義。本論文對匹配場聲源定位問題中的信號、噪聲和傳播過程分別進行了建模。信號和噪聲均選擇了零均值圓對稱的復(fù)高斯隨機模型。在該模型下,以最小化Bhattacharyya距離估計器為基礎(chǔ)推導(dǎo)出數(shù)學(xué)形式簡潔、對稱的匹配協(xié)方差估計器(Matched-Covariance Estimator,簡稱MCE)，該估計器通過匹配模型拷貝協(xié)方差矩陣和測量數(shù)據(jù)協(xié)方差矩陣的方式來進行參數(shù)估計,使得MCE具有對多秩信號參數(shù)估計的能力。對于噪聲模型而言,本論文依據(jù)匹配場處理過程中的實際特點將噪聲建模為空間白的本地噪聲和空間相關(guān)的傳播噪聲場。其中傳播噪聲又可分為離散分布噪聲(如：點干擾噪聲)和連續(xù)分布噪聲(如：海面生成噪聲)o傳播噪聲因歷經(jīng)與聲源信號相類似的水聲信道傳播,存在空間相似性,而對匹配場處理方法提出額外的挑戰(zhàn)。在傳播模型方面,本論文針對三種典型聲源定位問題選取了三種各具代表性的模型：1)針對深海、自由場環(huán)境中的聲源定向問題,選擇了單模態(tài)平面波模型；2)針對淺海平穩(wěn)波導(dǎo)環(huán)境下的聲源定位問題,選擇了多模態(tài)全波場模型；3)針對淺海起伏聲場中的定位問題,選擇了最近發(fā)展的多模態(tài)、多相干模態(tài)組模型。本論文通過在不同類型聲源定位問題中,對不同代價準(zhǔn)則下的機器學(xué)習(xí)系統(tǒng)性能比較,來從不同的角度評估機器學(xué)習(xí)架構(gòu)下的匹配場聲源定位性能。綜合深海自由場、淺海全波場及淺海起伏聲場的聲源定位仿真結(jié)果,可以得出基于信息理論原則的MCE估計方法優(yōu)于傳統(tǒng)匹配場估計方法,因為：1)MCE能夠同時開發(fā)信號和噪聲的數(shù)據(jù)結(jié)構(gòu)；2)MCE不采用對噪聲抑制或抵消的操作,避免了在干擾源噪聲與聲源信號存在空間相似性時錯誤抵消信號的現(xiàn)象；3)MCE不限定信號空間的秩為“1”,可以完成對多秩信號的估計；及4)MCE不需要大量的持續(xù)數(shù)據(jù)來估計協(xié)方差矩陣,可以有效地緩解統(tǒng)計特性失配問題。
[Abstract]:This paper attempts to study the sound source location information from the sensor data measured in the problem from the perspective of learning machine. In the optimization algorithm under the guidance of machine learning in specific cost criteria by minimizing the error between the model and the actual measured data copy data structure learning. Conventional matched field processing (Matched-field processing, referred to as MFP) methods (such as: Bartlett correlator, the maximum likelihood estimator and minimum variance distortionless response estimator etc.) is through the search parameters in the parameters of the sound source location parameter space grid and selects the fuzzy estimator corresponding to the peak of the output as an estimate. The output of the fuzzy reciprocal can be regarded as a generalized error grid the search is a search strategy for most parameters, reflect the parameters to be estimated is the structural information of the data, so that machine learning include the The traditional matched field source localization methods. This paper will establish the matched field processing in machine learning framework, selecting principles of information theory to realize the cost criterion based on distance measurement model and the actual copy of the measurement data in the sense of information. At present there are many methods of information theory to measure the distance between the information, which is the most commonly used is the divergence. This thesis takes in information theory and signal detection in the widely used f- divergence of.F- divergence contains relative entropy is known in information theory, the symmetric form known as information divergence.F- divergence also contains the Hellinger integral, it can be a signal detection theory in the famous error probability limit the Chernoff Bound.Bhattacharyya Hellinger parameter is equal to the integral control coefficient is constant 1/2 results, it can be without loss of generality of signal detection problem The probability of error, the lower limit, the reciprocal of the natural logarithm of information can be measured the distance between the Bhattacharyya distance. Within the framework of information theory, this paper selects the Bhattacharyya distance as the cost criterion, and follow the minimum relative entropy obtained the maximum likelihood estimator of the way won the minimum Bhattacharyya distance estimator. While ensuring accurate characterization of the actual distribution of measurement the data of Gauss distribution can not, but it is usually the most reasonable choice. Gauss distribution is the central limit theorem for analytic theory analysis and easy to generate other distribution characteristics, making it extraordinary. Especially, when the stochastic process, two moments are known, the Gauss distribution can maximize Cramer-Rao any limit. So the optimization criterion of Cramer-Rao limit based on Gauss distribution has become a Min-Max optimization criterion, which is the minimum The maximum limit of Cramer-Rao. Because the Cramer-Rao limit is the unbiased estimation of variance theory limit was, the characteristics for the unbiased estimation of evaluation is of guiding significance for the performance. In this paper, the assumption that the signal and noise are subject to random process with zero mean Gauss distribution, the statistical properties of the data can be completely characterized by the covariance matrix. The matched field processing, limited signal stationary time, phase distortion free bandwidth and other factors, can only obtain valid data of limited samples. At the same time, through the maximum likelihood estimation method to obtain the sample covariance matrix will be due to limited data and real data and the error covariance matrix, leading to the statistical characteristics of mismatch, distortion data information. Therefore, the development can mismatch stability matched field processing method in statistical characteristics is of great significance. In this paper. The signal source localization problem, noise and propagation process were established. The signal and noise are chosen with zero mean circularly symmetric complex Gauss stochastic model. In this model, to minimize the distance based Bhattacharyya estimator is derived form simple, symmetric covariance estimator (Matched-Covariance Estimator, referred to as MCE), the estimator by matching the model copy covariance matrix and the covariance matrix of measurement data to estimate the parameters, the MCE has ability to estimate multi rank signal parameters. For the noise model, this paper on the basis of the actual characteristics, field in the process of modeling the spatial white noise of the local noise and spatial noise field related. The propagation of noise can be divided into discrete distribution of noise (such as: noise) and continuous noise (such as: sea surface generation Noise) of underwater acoustic channel o transmission noise and sound source signal after similar, spatial similarity, and the matched field method presents additional challenges. In the propagation model in this paper, three kinds of typical sound source localization problem has selected three representative models: 1) in the deep sea, sound source orientation free field environment, the choice of single mode plane wave model; 2) to solve the problem of sound source localization in shallow water waveguide stable environment, selection of multi modal full wave field model; 3) for localization in shallow sea undulating in the field, choose the multi modality of the recent development of multiple coherent mode group model. In the thesis, the problems of different types of sound source localization, comparison of different cost criteria of machine learning system performance, machine learning under the framework of matched field source localization performance from different angles. The comprehensive evaluation from the deep sea From the field, the simulation results of sound source localization in shallow water and shallow acoustic full wave field fluctuation, it can be based on the principles of information theory MCE estimation method is better than the traditional matched field estimation method, because: 1) the MCE data structure can also develop the signal and noise; 2) MCE is not used to the noise suppression or offset operation. To avoid the existence of Space Similarity error signal offset of the interference source in noise and sound source signal; 3) MCE does not limit the signal space rank as "1", can complete the estimation of multi rank signal; and 4) MCE does not need to continue a large amount of data to estimate the covariance matrix, can effectively alleviate the statistics the characteristics of mismatch problem.

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：TB56;TP181

【參考文獻】

相關(guān)博士學(xué)位論文 前2條

1 夏夢璐;淺水起伏環(huán)境中模型—數(shù)據(jù)結(jié)合水聲信道均衡技術(shù)[D];浙江大學(xué);2012年

2 肖專;復(fù)雜海洋環(huán)境匹配場源定位性能分析[D];浙江大學(xué);2011年

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本文編號：1725263