【摘要】：陣列信號(hào)處理具有波束控制靈活、信號(hào)增益高、干擾抑制能力強(qiáng)等優(yōu)點(diǎn),自適應(yīng)波束形成是陣列信號(hào)處理中的重要研究方向,通過自適應(yīng)的調(diào)整權(quán)值使陣列方向圖主瓣指向期望信號(hào)方向,零點(diǎn)對(duì)準(zhǔn)干擾方向,提高輸出信噪比。在實(shí)際應(yīng)用中,由于存在離散掃描間隔和陣元誤差失配造成的導(dǎo)向向量誤差以及有限采樣快拍數(shù)造成數(shù)據(jù)協(xié)方差矩陣估計(jì)誤差,因此,研究具有誤差魯棒性的穩(wěn)健自適應(yīng)波束形成算法具有重要研究意義。本文首先建立陣列信號(hào)處理數(shù)學(xué)模型,介紹自適應(yīng)波束形成算法基本思想。然后引入三種最優(yōu)波束形成準(zhǔn)則(MMSE、MSINR、LCMV)和幾種經(jīng)典的自適應(yīng)算法(LMS、RLS、GSC)。為了克服經(jīng)典自適應(yīng)算法對(duì)模型誤差敏感的缺點(diǎn),論文引入了在模型失配下仍能保證良好輸出性能的穩(wěn)健自適應(yīng)算法,介紹了LSMI、ESB、RCB三種經(jīng)典的穩(wěn)健自適應(yīng)算法,并分析其性能優(yōu)缺點(diǎn)。論文針對(duì)當(dāng)采樣快拍數(shù)據(jù)含有期望信號(hào)分量時(shí),現(xiàn)有一些算法性能衰落的缺點(diǎn),提出了兩種基于協(xié)方差矩陣重構(gòu)的穩(wěn)健自適應(yīng)算法。第一種算法采用聯(lián)合算法的思想,先利用Music空間譜方法重構(gòu)干擾噪聲協(xié)方差矩陣,去除采樣矩陣中期望信號(hào)分量,再通過求解優(yōu)化問題修正期望信號(hào)導(dǎo)向向量。仿真實(shí)驗(yàn)結(jié)果表明,該算法對(duì)低快拍誤差具有穩(wěn)健性,且增強(qiáng)了干擾抑制能力。另一種算法是對(duì)正交投影算法(OP)的改進(jìn),將利用Music空間譜估計(jì)方法重構(gòu)干擾噪聲協(xié)方差矩陣應(yīng)用到正交投影算法中。仿真實(shí)驗(yàn)結(jié)果表明,改進(jìn)OP算法解決了原OP算法在樣本數(shù)據(jù)含有期望信號(hào)分量時(shí)的信號(hào)相消問題,且減小了噪聲擾動(dòng)對(duì)算法性能的影響,增強(qiáng)了干擾抑制能力。
[Abstract]:Array signal processing has the advantages of flexible beam control, high signal gain and strong interference suppression ability. Adaptive beamforming is an important research direction in array signal processing. The main lobe of the array pattern is directed towards the desired signal direction and the zero point is aligned to the interference direction by adjusting the weights adaptively to improve the output signal-to-noise ratio (SNR). In practical application, the error of covariance matrix estimation is caused by the mismatch between discrete scan interval and element error, and because of the error of guide vector caused by the mismatch of discrete scanning interval and error of array element, and the estimation error of data covariance matrix caused by the limited sampling beat number. It is important to study robust adaptive beamforming algorithm with error robustness. In this paper, the mathematical model of array signal processing is established, and the basic idea of adaptive beamforming algorithm is introduced. Then three kinds of optimal beamforming criteria (MMSE,MSINR,LCMV) and several classical adaptive algorithms (LMS,RLS,GSC) are introduced. In order to overcome the shortcoming that classical adaptive algorithm is sensitive to model error, this paper introduces robust adaptive algorithm which can guarantee good output performance under model mismatch, and introduces three classical robust adaptive algorithms of LSMI,ESB,RCB. The advantages and disadvantages of the performance are analyzed. In this paper, two robust adaptive algorithms based on covariance matrix reconstruction are proposed to overcome the shortcomings of the performance fading of some existing algorithms when the sampled rapid-beat data contains the desired signal component. The first algorithm adopts the idea of joint algorithm. Firstly, the interference noise covariance matrix is reconstructed by Music spatial spectrum method, and the desired signal component is removed from the sampling matrix, and then the desired signal guidance vector is corrected by solving the optimization problem. The simulation results show that the algorithm is robust to low beat error and enhances the ability of interference suppression. The other algorithm is to improve the orthogonal projection algorithm (OP), which uses the Music space spectrum estimation method to reconstruct the interference noise covariance matrix in the orthogonal projection algorithm. The simulation results show that the improved OP algorithm solves the signal cancellation problem of the original OP algorithm when the sample data contains the desired signal component, reduces the influence of noise disturbance on the performance of the algorithm, and enhances the ability of interference suppression.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN911.7
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本文編號(hào)：2214773