【摘要】：基于均勻圓陣的MUSIC測(cè)向算法具有優(yōu)秀的性能,但是涉及的運(yùn)算復(fù)雜度很高,難以滿足實(shí)時(shí)性的要求。本文主要研究均勻圓陣的MUSIC快速測(cè)向算法,具體如下:(1) UCA-RB-MUSIC算法首先采用波束空間變換將均勻圓陣復(fù)雜的陣列流型變換為類似均勻線陣的范德蒙結(jié)構(gòu),然后采用一種酉變換實(shí)現(xiàn)波束空間數(shù)據(jù)協(xié)方差矩陣由復(fù)數(shù)域到實(shí)數(shù)域的轉(zhuǎn)換。本文針對(duì)UCA-RB-MUSIC算法中酉變換運(yùn)算復(fù)雜的問(wèn)題,提出一種新的酉變換矩陣,其在不損失性能的同時(shí)減少了運(yùn)算復(fù)雜度。(2)子空間估計(jì)在高分辨率測(cè)向算法中占有舉足輕重的地位,但是它一般需要計(jì)算陣元接收數(shù)據(jù)協(xié)方差矩陣并對(duì)其做特征分解,運(yùn)算復(fù)雜度很高。對(duì)此,本文首先研究了特征分解的硬件實(shí)現(xiàn)算法,并對(duì)單側(cè)Jacobi算法進(jìn)行改進(jìn),使其在迭代運(yùn)算的同時(shí)以很小的硬件開(kāi)銷實(shí)現(xiàn)特征向量按特征值大小的自動(dòng)排序。其次,本文研究了基于多級(jí)維納濾波(MSWF)的快速子空間估計(jì)技術(shù),并對(duì)其在低信噪比情況下性能急劇下降的問(wèn)題給出一種改進(jìn)方法。該方法改進(jìn)了低信噪比情況下的MSWF類子空間估計(jì)性能,但同時(shí)有一定概率引起偽峰的出現(xiàn)。(3)在研究波達(dá)方向估計(jì)問(wèn)題過(guò)程中一般假設(shè)信源數(shù)為己知的,然而其在實(shí)際應(yīng)用中通常是需要估計(jì)的。對(duì)此,本文研究了基于MSWF的信源數(shù)估計(jì)方法,并針對(duì)其在低信噪比情況下性能急劇下降的問(wèn)題提出一種改進(jìn)方法。(4)本文針對(duì)均勻圓陣的求根MUSIC算法中子空間估計(jì)過(guò)程運(yùn)算復(fù)雜、耗時(shí)大的問(wèn)題,引入基于MSWF的快速子空間估計(jì)技術(shù),同時(shí)對(duì)其遞推過(guò)程采用一種預(yù)處理方法,使運(yùn)算過(guò)程由復(fù)數(shù)運(yùn)算轉(zhuǎn)為高效的實(shí)數(shù)運(yùn)算。均勻圓陣中MSWF類子空間估計(jì)方法依然存在低信噪比情況下性能急劇下降的問(wèn)題,對(duì)此本文提出另一種改進(jìn)方法。該方法針對(duì)性能下降的本質(zhì)原因,大幅削弱噪聲對(duì)子空間估計(jì)性能的影響,但是該方法需要預(yù)估噪聲功率。MSWF類子空間估計(jì)方法與均勻圓陣求根MUSIC算法有多種組合方式,文章最后通過(guò)仿真實(shí)驗(yàn)分析了多種組合方式的測(cè)向性能。
[Abstract]:The MUSIC direction finding algorithm based on uniform circular array has excellent performance, but the computational complexity involved is very high, so it is difficult to meet the requirement of real-time. In this paper, the MUSIC fast direction finding algorithm for uniform circular array is studied. The main results are as follows: (1) the complex array flow pattern of uniform circular array is transformed by UCA-RB-MUSIC algorithm into a Vandermonde structure similar to uniform linear array. Then a unitary transformation is used to transform the covariance matrix of beamspace data from complex domain to real field. Aiming at the complexity of unitary transformation in UCA-RB-MUSIC algorithm, a new unitary transformation matrix is proposed in this paper, which reduces the computational complexity without loss of performance. (2) subspace estimation plays an important role in high-resolution direction-finding algorithm. However, it usually needs to compute and decompose the covariance matrix of data received by elements, so it has a high computational complexity. In this paper, the hardware implementation algorithm of feature decomposition is studied firstly, and the one-sided Jacobi algorithm is improved to realize the automatic sorting of eigenvector by eigenvalue at the same time of iterating operation with very small hardware overhead. Secondly, the fast subspace estimation technique based on multistage Wiener filter (MSWF) is studied, and an improved method is proposed to solve the problem that its performance drops sharply in the case of low signal-to-noise ratio (SNR). This method improves the performance of MSWF subspace estimation under low SNR, but at the same time there is a certain probability to cause pseudo peaks. (3) in the study of DOA estimation, it is generally assumed that the number of sources is known. However, it usually needs to be estimated in practical applications. In this paper, the source number estimation method based on MSWF is studied, and an improved method is proposed to solve the problem of the sharp deterioration of its performance in the case of low signal-to-noise ratio. (4) the neutron space estimation process of MUSIC algorithm for uniform circular array is complex. In this paper, a fast subspace estimation technique based on MSWF is introduced, and a preprocessing method is used in the recursive process to change the operation from complex operation to efficient real number operation. The MSWF subspace estimation method in uniform circular array still has the problem of sharp degradation of performance in the case of low signal-to-noise ratio (SNR). In this paper, another improved method is proposed. The effect of noise on the performance of subspace estimation is greatly weakened for the essential cause of performance degradation. However, this method needs to estimate the noise power. MSWF-class subspace estimation method and uniform circular array root-finding MUSIC algorithm have many combinations. Finally, the performance of multiple combination methods is analyzed by simulation experiments.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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本文編號(hào)：2150447