基于協(xié)方差矩陣和導向矢量不確定性的魯棒波束形成技術
發(fā)布時間：2018-10-14 09:03
【摘要】：自適應波束形成技術是陣列信號處理中一個重要的研究方向,其在通信、雷達、聲吶、語音處理、醫(yī)學成像等領域,都有著廣闊的應用前景。傳統(tǒng)的波束形成方法旨在保持期望信號一定的前提下抑制干擾。然而,在現(xiàn)實應用中,由于環(huán)境、信號源以及天線陣元的真實情況與預設情況存在偏差,傳統(tǒng)波束形成方法的性能會因這些偏差而急劇下降。因此,研究如何提高魯棒性能對自適應波束形成方法而言意義重大。近幾十年,研究人員提出了許多具有魯棒性的自適應波束形成方法,對角加載是其中一種重要的方法。該方法在采樣數(shù)少于陣列傳感器數(shù)目導致樣本協(xié)方差矩陣不可逆時仍能獲得較好的性能。本論文在對角加載范疇內,提出了一種新的魯棒波束形成方法,主要工作內容如下:(1)介紹了波束形成的相關基礎知識,介紹了幾種常見的波束形成方法,給出了其主要推導過程,并結合推導過程對其特點和缺陷進行了分析說明。(2)詳細介紹了幾種重要的對角加載范疇內的魯棒自適應波束形成方法,介紹了其研究狀況,推導過程,計算復雜度,并對其性能優(yōu)劣點進行了比較分析。(3)提出了一種新的可變對角加載魯棒自適應波束形成方法。通過研究分析如最差性能優(yōu)化和RCB等考慮導向矢量不確定性集的魯棒方法,發(fā)現(xiàn)這些對角加載范疇內的方法都只單獨考慮了導向矢量的不確定性。因此,本文所提方法的一個創(chuàng)新點在于,假設協(xié)方差矩陣和導向矢量都存在誤差,并對這兩種誤差分別施加約束條件,進而最大化SINR。根據(jù)約束條件進行推導,本文得到一個最小最大優(yōu)化問題,根據(jù)最優(yōu)化的相關結論,本文對模型進行放縮,從而得到一個最大最小優(yōu)化問題。最終,通過KKT優(yōu)化條件求解出這個最大最小優(yōu)化問題。此外,由于對角加載方法其作用的實質是通過人工投影白噪聲以降低輸入信噪比,導致大的加載量雖可以增強魯棒性能卻使系統(tǒng)對干擾和噪聲的抑制能力下降。因此,本論文的另一個創(chuàng)新點在于,所提方法算出的可變對角加載量考慮了特征值的大小,即大特征值對應相對小的加載量,而小特征值對應相對大的加載量。(4)給出了本文所提方法與前述幾種對角加載方法的仿真結果,并對仿真結果進行了分析比較,說明了所提方法性能更優(yōu)。
[Abstract]:Adaptive beamforming technology is an important research direction in array signal processing. It has a broad application prospect in communication, radar, sonar, speech processing, medical imaging and other fields. The traditional beamforming method is designed to suppress interference on the premise of keeping the desired signal. However, in practical applications, the performance of traditional beamforming methods will be drastically reduced due to the deviation between the environment, signal source and antenna array elements and the preset conditions. Therefore, it is important to study how to improve robust performance for adaptive beamforming. In recent decades, many robust adaptive beamforming methods have been proposed, among which diagonal loading is one of the most important methods. The proposed method can still achieve good performance when the number of samples is less than the number of array sensors and the sample covariance matrix is irreversible. In this paper, a new robust beamforming method is proposed under diagonal loading. The main work is as follows: (1) the basic knowledge of beamforming is introduced, and several common beamforming methods are introduced. The main derivation process is given, and its characteristics and defects are analyzed. (2) several important robust adaptive beamforming methods in diagonal loading category are introduced in detail, and their research status and derivation process are introduced. The computational complexity and performance advantages and disadvantages are compared and analyzed. (3) A new robust adaptive beamforming method with variable diagonal loading is proposed. Based on the analysis of robust methods such as worst performance optimization and RCB, it is found that these methods in diagonally loaded category only consider the uncertainty of guidance vector. Therefore, one of the innovations of the method proposed in this paper is to assume that there are errors in both the covariance matrix and the guidance vector, and to impose constraints on the two errors to maximize the SINR.. According to the constraint conditions, this paper obtains a minimum and maximum optimization problem. According to the relevant conclusions of optimization, the model is scaled down, and a maximum and minimum optimization problem is obtained. Finally, the maximum and minimum optimization problem is solved by KKT optimization condition. In addition, the essence of diagonal loading method is to reduce the input SNR by artificial projection of white noise, which results in a large amount of loading can enhance the robust performance but reduce the ability of the system to suppress interference and noise. Therefore, another innovation of this paper is that the variable diagonal load calculated by the proposed method takes into account the magnitude of the eigenvalue, that is, the large eigenvalue corresponds to a relatively small amount of loading. The small eigenvalues correspond to a relatively large amount of loading. (4) the simulation results of the proposed method and the above diagonal loading methods are given, and the simulation results are analyzed and compared, which shows that the proposed method has better performance.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.7
[Abstract]:Adaptive beamforming technology is an important research direction in array signal processing. It has a broad application prospect in communication, radar, sonar, speech processing, medical imaging and other fields. The traditional beamforming method is designed to suppress interference on the premise of keeping the desired signal. However, in practical applications, the performance of traditional beamforming methods will be drastically reduced due to the deviation between the environment, signal source and antenna array elements and the preset conditions. Therefore, it is important to study how to improve robust performance for adaptive beamforming. In recent decades, many robust adaptive beamforming methods have been proposed, among which diagonal loading is one of the most important methods. The proposed method can still achieve good performance when the number of samples is less than the number of array sensors and the sample covariance matrix is irreversible. In this paper, a new robust beamforming method is proposed under diagonal loading. The main work is as follows: (1) the basic knowledge of beamforming is introduced, and several common beamforming methods are introduced. The main derivation process is given, and its characteristics and defects are analyzed. (2) several important robust adaptive beamforming methods in diagonal loading category are introduced in detail, and their research status and derivation process are introduced. The computational complexity and performance advantages and disadvantages are compared and analyzed. (3) A new robust adaptive beamforming method with variable diagonal loading is proposed. Based on the analysis of robust methods such as worst performance optimization and RCB, it is found that these methods in diagonally loaded category only consider the uncertainty of guidance vector. Therefore, one of the innovations of the method proposed in this paper is to assume that there are errors in both the covariance matrix and the guidance vector, and to impose constraints on the two errors to maximize the SINR.. According to the constraint conditions, this paper obtains a minimum and maximum optimization problem. According to the relevant conclusions of optimization, the model is scaled down, and a maximum and minimum optimization problem is obtained. Finally, the maximum and minimum optimization problem is solved by KKT optimization condition. In addition, the essence of diagonal loading method is to reduce the input SNR by artificial projection of white noise, which results in a large amount of loading can enhance the robust performance but reduce the ability of the system to suppress interference and noise. Therefore, another innovation of this paper is that the variable diagonal load calculated by the proposed method takes into account the magnitude of the eigenvalue, that is, the large eigenvalue corresponds to a relatively small amount of loading. The small eigenvalues correspond to a relatively large amount of loading. (4) the simulation results of the proposed method and the above diagonal loading methods are given, and the simulation results are analyzed and compared, which shows that the proposed method has better performance.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.7
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