【摘要】：自適應(yīng)波束形成(Beamforming)是陣列信號處理領(lǐng)域中的一個(gè)重要研究方向,其利用空間多傳感器陣列所構(gòu)成的處理系統(tǒng)對空間信號進(jìn)行發(fā)射或接收,在雷達(dá)、聲納、通信系統(tǒng)、智能家電以及智能會(huì)議系統(tǒng)中有著廣泛的應(yīng)用。但是,自適應(yīng)波束形成算法的性能依賴于對入射信號、陣列以及環(huán)境的假設(shè),對假設(shè)的準(zhǔn)確性非常敏感。在實(shí)際的工程應(yīng)用中,誤差總是存在的,因此研究對誤差穩(wěn)健的波束形成算法很有必要。 目前比較流行的穩(wěn)健算法主要分為三類：特征子空間類、對角加載類以及凸優(yōu)化類。第一類算法在低信噪比條件下性能損失嚴(yán)重,并且需要已知信號源的數(shù)目；第二類算法的缺點(diǎn)主要在于加載因子和實(shí)際誤差的上下限沒有可靠的確定方法；最后一類算法是近幾年研究得最多的,這類算法的提出使波束形成算法的性能得到了很大的提升,但仍然存在一些缺陷,比如對陣元位置以及多徑等誤差比較敏感。本文的研究主要集中在特征子空間類和凸優(yōu)化類算法上,針對實(shí)際應(yīng)用中存在的誤差提出了三種穩(wěn)健的波束形成算法,主要內(nèi)容和創(chuàng)新點(diǎn)如下： 1.針對多約束線性約束最小方差算法輸出信干噪比損失的問題,提出了基于復(fù)數(shù)約束的線性約束最小方差穩(wěn)健波束形成算法。算法采用復(fù)數(shù)約束,并且約束是一個(gè)變量,最優(yōu)的約束值可以在最大信噪比準(zhǔn)則下求解得到,文中對算法進(jìn)行了詳細(xì)的推導(dǎo)。通過計(jì)算機(jī)仿真實(shí)驗(yàn)驗(yàn)證了該方法的有效性,算法有良好的輸出性能,并且計(jì)算復(fù)雜度和常規(guī)的穩(wěn)健波束形成算法相當(dāng)。 2.協(xié)方差矩陣估計(jì)是波束形成算法需要解決的關(guān)鍵問題之一,本文針對低快拍條件下,協(xié)方差矩陣估計(jì)誤差較大的問題,提出了一種基于先驗(yàn)知識的穩(wěn)健自適應(yīng)波束形成算法,這種算法先利用矩陣重構(gòu)的方法構(gòu)造出不包含期望信號的協(xié)方差矩陣,再利用采樣數(shù)據(jù)協(xié)方差矩陣來聯(lián)合估計(jì)理論最優(yōu)的協(xié)方差矩陣。對比實(shí)驗(yàn)表明該算法在低快拍條件下具有良好的輸出性能。 3.目前有學(xué)者提出了一種迭代的穩(wěn)健波束形成算法,但當(dāng)干擾的干噪比比期望信號的輸入信噪比大時(shí),算法可能收斂到干擾的方向。針對這一問題,本文提出了一種新算法,算法采用一種新的信號子空間估計(jì)方法,無需已知信號源的數(shù)目,并且在低信噪比條件仍然有效。用此方法可以得到干擾加噪聲投影矩陣,然后利用期望信號與干擾加噪聲空間的正交性對期望信號導(dǎo)向矢量進(jìn)行估計(jì),避免算法收斂到干擾方向。在迭代過程中,采用放寬的約束,從而保證期望信號在約束空間中,避免算法收斂不到最優(yōu)解。最后通過仿真實(shí)驗(yàn),驗(yàn)證了所提算法的有效性,并對算法進(jìn)行了對比分析和總結(jié)。
[Abstract]:Adaptive beamforming (Beamforming) is an important research direction in the field of array signal processing. It uses a processing system composed of spatial multi-sensor arrays to transmit or receive space signals in radar, sonar and communication systems. Intelligent home appliances and intelligent conference systems have a wide range of applications. However, the performance of adaptive beamforming algorithm depends on the assumption of incident signal, array and environment, and is very sensitive to the accuracy of hypothesis. In practical engineering applications, the error always exists, so it is necessary to study the error robust beamforming algorithm. At present, the popular robust algorithms are divided into three categories: feature subspace class, diagonal loading class and convex optimization class. Under the condition of low signal-to-noise ratio (SNR), the first kind of algorithm has serious performance loss and needs the number of known signal sources, and the disadvantage of the second type algorithm is that there is no reliable method to determine the upper and lower bound of loading factor and actual error. The last kind of algorithm has been studied most in recent years. The performance of beamforming algorithm has been greatly improved by this kind of algorithm, but there are still some shortcomings, such as the error of element position and multipath is sensitive. In this paper, we mainly focus on the feature subspace class and convex optimization algorithm, and propose three robust beamforming algorithms for the errors existing in practical applications. The main contents and innovations are as follows: 1. A linear constrained minimum variance robust beamforming algorithm based on complex constraints is proposed to solve the problem of output signal-to-noise ratio loss of multi-constraint linear constrained minimum variance algorithm. The complex constraint is used in the algorithm, and the constraint is a variable. The optimal constraint value can be obtained under the maximum signal-to-noise ratio criterion. In this paper, the algorithm is deduced in detail. The effectiveness of the proposed method is verified by computer simulation. The algorithm has good output performance and the computational complexity is similar to that of the conventional robust beamforming algorithm. 2. Covariance matrix estimation is one of the key problems to be solved in beamforming algorithm. In this paper, a robust adaptive beamforming algorithm based on prior knowledge is proposed to solve the problem of large error of covariance matrix estimation under the condition of low fast beat. In this algorithm, the covariance matrix without the desired signal is constructed by the method of matrix reconstruction, and then the covariance matrix of sampled data is used to jointly estimate the theoretical optimal covariance matrix. The experimental results show that the proposed algorithm has good output performance under the condition of low speed beat. 3. At present, an iterative robust beamforming algorithm is proposed. However, when the interference noise ratio is larger than the expected signal-to-noise ratio, the algorithm may converge to the direction of interference. In order to solve this problem, a new algorithm is proposed in this paper. The algorithm adopts a new method of signal subspace estimation without the number of known signal sources, and is still valid under low SNR conditions. The projection matrix of interference plus noise can be obtained by this method, and then the guidance vector of desired signal can be estimated by using the orthogonality of expected signal and interference plus noise space to avoid convergence to the direction of interference. In the iterative process, a relaxed constraint is used to ensure that the desired signal is in the constraint space, so that the algorithm does not converge to the optimal solution. Finally, the validity of the proposed algorithm is verified by simulation experiments, and the algorithm is compared and summarized.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

【共引文獻(xiàn)】

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本文編號：2436656