發(fā)布時(shí)間：2018-03-30 14:19

  本文選題：波達(dá)方向　切入點(diǎn)：差分演化　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2017年博士論文

【摘要】：在存在多個(gè)窄帶源的環(huán)境中,遠(yuǎn)場(chǎng)和近場(chǎng)信源定位存在諸多挑戰(zhàn)。為分析信源定位的性能,常見的挑戰(zhàn)有:陣列輸出的快拍,未知參數(shù)的聯(lián)合估計(jì),未知參數(shù)的匹配,計(jì)算復(fù)雜度,對(duì)噪聲的魯棒性,信源與陣列之間距離的估計(jì),陣列擾動(dòng)和多維波達(dá)方向(DOA)。這里提到的一些挑戰(zhàn)可以在系統(tǒng)建模中得到解決,例如陣列的幾何結(jié)構(gòu),陣元的間隔和待測(cè)的信源數(shù)量,其他的挑戰(zhàn)則可以在算法的層面上進(jìn)行處理。選擇近場(chǎng)窄帶信源定位是受到室內(nèi)通信和信源定位,超聲波成像,電子監(jiān)控,射頻識(shí)別(RFID)通信,水下信源定位和地震勘探等領(lǐng)域不斷增長(zhǎng)的應(yīng)用需求的啟發(fā)。本論文中,主要貢獻(xiàn)之一是在近場(chǎng)信源定位中僅使用陣列輸出的單快拍數(shù)據(jù)進(jìn)行參數(shù)估計(jì),這使其可以在實(shí)時(shí)應(yīng)用中使用。此外,我們通過(guò)利用演化計(jì)算的優(yōu)勢(shì),實(shí)現(xiàn)無(wú)需匹配的距離和DOA聯(lián)合估計(jì)。文中使用均勻線陣(ULA)和L-型陣列等陣列構(gòu)型,是因其具有成本效益,計(jì)算簡(jiǎn)便和易于使用等性質(zhì)。本文的主要貢獻(xiàn)簡(jiǎn)要總結(jié)如下:1.均勻線陣(ULA)近場(chǎng)窄帶信源定位(1DDOAs和距離)的統(tǒng)計(jì)分析和建模。當(dāng)窄帶信源存在于陣列的菲涅耳區(qū)(近場(chǎng))時(shí),信源定位問(wèn)題變得更加復(fù)雜。由于信源入射的波前變成球面,同時(shí)需要信源的距離和到達(dá)角信息才能實(shí)現(xiàn)準(zhǔn)確定位。這將使聯(lián)合估計(jì)的未知參數(shù)數(shù)目翻倍,并使計(jì)算過(guò)程復(fù)雜化�，F(xiàn)有的近場(chǎng)窄帶信源定位模型需要大量的陣列輸出快拍。此外,大多數(shù)現(xiàn)有模型不能聯(lián)合估計(jì)未知參數(shù),而是需要逐個(gè)進(jìn)行估計(jì)。這也使得這些方法在需要實(shí)時(shí)性的應(yīng)用中無(wú)法使用。為了克服大量快拍的需求,提出了一種稱為差分演化的演化技術(shù),采用均方誤差作為適應(yīng)度評(píng)估函數(shù)。所提算法十分高效,能夠在基于均勻線陣的近場(chǎng)窄帶信源定位中使用單快拍進(jìn)行未知參數(shù)的聯(lián)合估計(jì)。所提算法的統(tǒng)計(jì)分析是通過(guò)大量的蒙特卡羅模擬進(jìn)行的。仿真結(jié)果表明,所提出的方法更接近于Cramer-Rao界,且隨著信噪比的增加逐步趨近。此外,結(jié)果表明,當(dāng)信源遠(yuǎn)離陣列時(shí),根據(jù)遠(yuǎn)場(chǎng)中來(lái)波距離無(wú)限遠(yuǎn)的理論,所提算法表現(xiàn)會(huì)受到影響。且當(dāng)信源的數(shù)量大于所使用的陣元數(shù)時(shí),所提方法失效,因?yàn)檫@是已經(jīng)變成一個(gè)欠定問(wèn)題。2.存在陣元位置擾動(dòng)的情況下,均勻線陣的增強(qiáng)建模和近場(chǎng)窄帶信源定位性能的統(tǒng)計(jì)分析。通常,在陣列信號(hào)處理中,傳感器位置被認(rèn)為是已知的。在實(shí)際情況下,外部因素和制造精度限制會(huì)導(dǎo)致被稱為陣列擾動(dòng)的傳感器位置誤差。使用具有傳感器位置擾動(dòng)的陣列會(huì)降低參數(shù)估計(jì)的性能和精度。一些現(xiàn)有的模型通過(guò)陣列預(yù)校準(zhǔn)精確地獲得傳感器位置,然后進(jìn)行信源定位。為了避免陣列預(yù)校準(zhǔn)的需求,針對(duì)存在隨機(jī)陣元位置誤差的均勻線陣的近場(chǎng)窄帶信源定位進(jìn)行建模。在這種情況下,未知參數(shù)的數(shù)量是信源數(shù)量的兩倍多。為了簡(jiǎn)化,將該過(guò)程分為三個(gè)步驟。首先,在假設(shè)不存在傳感器位置誤差的情況下,聯(lián)合估計(jì)距離和DO As。由于實(shí)際中是存在傳感器位置誤差的,這些參數(shù)估計(jì)結(jié)果并不準(zhǔn)確。第二步,使用上面估計(jì)出的距離和DOAs作為校準(zhǔn)源來(lái)估計(jì)陣元位置擾動(dòng)。然后,考慮到第二步中估計(jì)出的陣元位置不確定性,對(duì)近場(chǎng)窄帶信源的DOAs和距離進(jìn)行更新。由于適應(yīng)度函數(shù)為均方誤差的差分演化算法所具有的能力,有效性,易用性和單快拍可收斂到最優(yōu)解的特性,將其作為全局優(yōu)化器。大量的蒙特卡羅模擬及其統(tǒng)計(jì)分析展示了所提方法的有效性。所提方法的實(shí)驗(yàn)結(jié)果與其他方法進(jìn)行以及Cramer-Rao界進(jìn)行了比較。結(jié)果表明,即使在傳感器擾動(dòng)的情況下,所提出的方法也優(yōu)于其他方法,并且趨近于Cramer-Rao界。3.在不使用任何配對(duì)匹配條件下,將均勻線性陣列的統(tǒng)計(jì)分析和建模擴(kuò)展到L型陣列,用于聯(lián)合估計(jì)近場(chǎng)窄帶源的距離和二維DOA。對(duì)近場(chǎng)窄帶信源距離和二維DOAs無(wú)需匹配的聯(lián)合估計(jì)中,將均勻線陣拓展到L-型陣列的統(tǒng)計(jì)分析和建模歷經(jīng)數(shù)十年,二維波達(dá)方向估計(jì)現(xiàn)已頗受重視�，F(xiàn)有的二維陣列幾何構(gòu)型包括圓形陣列,平面陣列,球形陣列等。隨著DOA估計(jì)的維數(shù)增加,估計(jì)過(guò)程的計(jì)算復(fù)雜度持續(xù)受陣列幾何構(gòu)型的影響,DOAs配對(duì)匹配變得至關(guān)重要,甚至?xí)䦟?dǎo)致配對(duì)不準(zhǔn)確和角度估計(jì)性能差。用于二維DOA估計(jì)的現(xiàn)有模型需要二維搜索和非線性優(yōu)化的匹配算法。為克服匹配的困難,并聯(lián)合估計(jì)近場(chǎng)窄帶源的范圍和二維DOA(即俯仰角和方位角),提出了一種由兩個(gè)ULA首尾垂直相接而成L-型平面陣列。與矩形陣列和圓形陣列相比,L型陣列在覆蓋區(qū)域和使用上具有優(yōu)勢(shì),因?yàn)樗恍枰^少的陣元。使用L型陣列的另一個(gè)優(yōu)點(diǎn)是能通過(guò)將陣列解耦為兩個(gè)ULA來(lái)獨(dú)立估計(jì)DOA從而得到二維DOA。由于適應(yīng)度函數(shù)為均方誤差的差分演化算法不需要譜峰搜索,額外的角度匹配過(guò)程和具有實(shí)時(shí)優(yōu)勢(shì)的單快拍收斂特性,將其由于優(yōu)化估計(jì)過(guò)程。理論分析和實(shí)驗(yàn)結(jié)果表明,即使在未知參數(shù)是信源數(shù)三倍的情況下,所提算法在俯仰角,方位角和距離的估計(jì)中仍優(yōu)于其他算法,并趨近于Cramer-Rao界。
[Abstract]:In the presence of multiple narrowband sources in the far field and near field source localization has many challenges. To analyze the performance of source location, the common challenges: the array output snapshots, joint estimation of unknown parameters, unknown parameters, computational complexity, robustness to noise, estimation of the distance between the source with the array, array perturbations and multidimensional direction of arrival (DOA). Some of the challenges mentioned here can be solved in the system modeling, the geometric structure of array for example, the number of array element spacing and measured the source, other challenges can be processed in the algorithm level. Selection of near-field narrowband source localization by indoor communication and source localization, ultrasonic imaging, electronic monitoring, radio frequency identification (RFID) communication, inspired applications field of underwater source localization and seismic exploration growing. In this thesis, the main contributions is in the past A single snapshot data array output using only the field source localization in parameter estimation, which can be used in real-time applications. In addition, we use evolutionary computing, without matching distance and DOA joint estimation. Using the uniform linear array (ULA) and L- type array array configuration. Because of its cost-effective, simple calculation and easy to use. The main contributions of this paper are briefly summarized as follows: 1. uniform linear array (ULA) near-field narrowband source localization (1DDOAs and distance) of the statistical analysis and modeling. When the Fresnel zone exists in the array of narrowband sources (near field), the source localization problem become more complex. Because the source of incident wavefront into a spherical surface, and the source of the distance and angle of arrival information to achieve accurate positioning. This will double the number of joint estimation of unknown parameters, and the calculation process of the existing complex. Near-field narrowband source localization model needs a large number of snapshots of array output. In addition, the joint estimation of unknown parameters in most existing models can not be, but need one by one estimate. It also makes these methods cannot be used in applications requiring real-time demand. In order to overcome a lot of snapshots, this paper proposes a model called differential evolution evolution technology, using mean square error as the fitness evaluation function. The proposed algorithm is very efficient in estimation of near-field narrowband source localization using uniform linear array in a single snapshot of the unknown parameters. The proposed algorithm based on the statistical analysis is simulated by a Monte Carlo. The simulation results show that the proposed method the more close to Cramer-Rao, and with the increase of the SNR gradually approaching. In addition, results show that when the source is far away from the array, according to the far field wave to infinite distance theory, The proposed algorithm performance will be affected. And when the number of array source quantity is greater than the use of the proposed method, the failure, because it has become a underdetermined problem of.2. array element position disturbance, statistical analysis of enhanced modeling of uniform linear array and near-field narrowband source localization performance. Usually, in array signal processing, sensor position is assumed to be known. In the actual situation, the external factors and the accuracy of manufacturing restrictions may lead to be called sensor position error array perturbations. With the sensor array position disturbance will reduce the parameter estimation accuracy and performance. Some of the existing model to obtain accurately the position sensor through the array of pre calibration, then the source location. In order to avoid pre array calibration needs, modeling the near-field narrowband source localization random uniform linear array element position error matrix. 鍦ㄨ繖縐嶆儏鍐典笅,鏈煡鍙傛暟鐨勬暟閲忔槸淇℃簮鏁伴噺鐨勪袱鍊嶅.涓轟簡(jiǎn)綆，

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