本文選題：電子戰(zhàn) + 超分辨測(cè)向�。� 參考：《江蘇科技大學(xué)》2017年碩士論文

【摘要】：現(xiàn)代電子戰(zhàn)離不開(kāi)對(duì)目標(biāo)的精確測(cè)向,隨著電磁環(huán)境的日益復(fù)雜和電子偵察系統(tǒng)指標(biāo)的不斷提高,傳統(tǒng)測(cè)向方法已經(jīng)難以適應(yīng)現(xiàn)代戰(zhàn)爭(zhēng)需求。超分辨測(cè)向算法以其優(yōu)越的性能,已在一些實(shí)時(shí)性要求較低的系統(tǒng)中得到應(yīng)用,隨著軟硬件實(shí)現(xiàn)能力的提高,使其在電子偵察領(lǐng)域的應(yīng)用成為可能。本文以艦載復(fù)雜環(huán)境下電子戰(zhàn)測(cè)向接收分機(jī)為應(yīng)用背景,開(kāi)展以多重信號(hào)分類(lèi)(MUSIC)算法為代表的子空間類(lèi)分解算法、低陣元數(shù)陣列測(cè)向性能,特定陣形下解相干算法以及算法實(shí)現(xiàn)等方面的研究工作,主要內(nèi)容包括:1.分析MUSIC算法的基本原理與改進(jìn)分類(lèi)。簡(jiǎn)述了MUSIC算法適用的信號(hào)類(lèi)型,分析了算法原理與特點(diǎn),并給出了一種高效的噪聲功率估計(jì)方法。2.細(xì)致比較了低陣元數(shù)天線(xiàn)陣形的測(cè)向性能。采用降維投影的方法,提出了均勻圓陣“無(wú)模糊”半徑選取方法,利用陣列流形與子空間的相關(guān)系數(shù)簡(jiǎn)化了陣列誤差的表示方法;從抗模糊性能、測(cè)向精度、各向等效性以及陣列誤差敏感度等角度對(duì)奇偶陣元數(shù)均勻圓陣和三種一維線(xiàn)性陣列的測(cè)向性能進(jìn)行了分析、對(duì)比與仿真。3.綜合考慮成本與測(cè)向性能,研究本課題適用的解相干信號(hào)測(cè)向方法。分析了經(jīng)典解相干算法的特性,將前后向空間平滑算法拓展到二維陣列,提出了中心對(duì)稱(chēng)平滑算法,該算法是一種能夠提高陣元復(fù)用率的二維解相干算法;分析了不同角度入射相干信號(hào)對(duì)信號(hào)源數(shù)目估計(jì)的影響。4.拆分研究MUSIC算法的各模塊的實(shí)現(xiàn)架構(gòu),以及方案的快速實(shí)現(xiàn)。分析并優(yōu)化了現(xiàn)有奇異值分解算法的并行實(shí)現(xiàn)方案,給出了FPGA邏輯加速部分設(shè)計(jì)方案;為了均衡延遲與資源消耗,選用了三級(jí)細(xì)粒度的譜峰搜索方法;使用Xilinx SDSo C套件,在Z-Turn平臺(tái)上完成算法的快速實(shí)現(xiàn)。
[Abstract]:Modern electronic warfare is inseparable from the accurate direction finding of targets. With the increasing complexity of electromagnetic environment and the continuous improvement of electronic reconnaissance system, the traditional direction finding methods have been difficult to meet the needs of modern warfare. Super-resolution direction finding algorithm has been applied in some systems with low real-time requirements due to its superior performance. With the improvement of the ability of software and hardware, it is possible to apply it in the field of electronic reconnaissance. In this paper, based on the application background of electronic warfare direction-finding extension in shipborne complex environment, the subspace class decomposition algorithm, which is represented by multiplex signal classification and MUSIC-based algorithm, is developed, and the direction finding performance of low array is obtained. The research work on decoherence algorithm and algorithm implementation under specific formation includes: 1. The basic principle and improved classification of MUSIC algorithm are analyzed. This paper briefly describes the signal types suitable for the MUSIC algorithm, analyzes the principle and characteristics of the algorithm, and presents an efficient noise power estimation method .2. The direction-finding performance of antenna array with low element number is compared in detail. By using the method of reducing dimension projection, the method of selecting the radius of uniform circular array "without fuzzy" is put forward, and the expression method of array error is simplified by using the correlation coefficient between array manifold and subspace. The performance of uniform circular array with odd and even array elements and three kinds of one-dimensional linear array are analyzed from the angles of equivalence and sensitivity of array error. Considering the cost and direction finding performance, this paper studies the direction finding method of decoherence signal. After analyzing the characteristics of classical de-coherent algorithm, the forward and backward spatial smoothing algorithm is extended to two-dimensional array, and a centrosymmetric smoothing algorithm is proposed, which is a two-dimensional decoherence algorithm which can improve the multiplexing rate of array elements. The influence of incident coherent signals at different angles on the number of signal sources is analyzed. This paper studies the implementation framework of each module of MUSIC algorithm and the fast implementation of the scheme. The parallel implementation scheme of the existing singular value decomposition algorithm is analyzed and optimized, and the design scheme of FPGA logic acceleration part is given. In order to balance delay and resource consumption, the three-level fine-grained spectral peak search method is selected, and the Xilinx SDSo C suite is used. Complete the fast implementation of the algorithm on the Z-Turn platform.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN97;TN911.7

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