【摘要】：雷達(dá)信號分選是將截獲到的多個混合在一起的雷達(dá)脈沖信號分選至同一輻射源類別之中。隨著電磁環(huán)境的不斷復(fù)雜化,大量的輻射源信號導(dǎo)致的脈沖混疊、參數(shù)之間高相關(guān)度、調(diào)制方式復(fù)雜多樣,傳統(tǒng)的依賴幾種常規(guī)信號參數(shù)進行分選的方法已經(jīng)逐漸變得不能適應(yīng)當(dāng)前情況。本文主要研究一種基于獨立分量分析的雷達(dá)信號分選方法,其優(yōu)勢在于只需要信源之間是統(tǒng)計獨立的,就可以將分屬不同信源的信號分選出來。本文所做的主要工作有:研究了傳統(tǒng)的信號分選方法,分析了傳統(tǒng)算法在現(xiàn)代復(fù)雜電磁環(huán)境下,存在的局限性以及其存在局限性的原因。分析了盲源分離算法,以及其能夠適用于信號分選的原因和原理,并將基于負(fù)熵的快速獨立分量分析算法引入信號分選。先將截獲信號做歸一化和白化預(yù)處理,然后用獨立分量分析進行分離,對分離出的信號估計參數(shù)并將不同時間段參數(shù)相同的進行歸類,達(dá)到分選目的。經(jīng)過仿真實驗證實,這種分選方法具有良好性能。同時,將快速獨立分量分析分選算法與傳統(tǒng)分選算法做對比,從各個方面證實了快速獨立分量分析分選算法的優(yōu)勢。多通道接收機并不一定能夠滿足信號接收通道大于信源數(shù)的理想正定問題。本文也提出了在單通道情況下,將經(jīng)驗?zāi)Ｊ椒纸馀c獨立分量分析相結(jié)合并應(yīng)用于信號分選的方法。先用經(jīng)驗?zāi)Ｊ椒纸馑惴▽⒍嘤蚧旌系慕邮招盘柗纸獬啥鄠�本征模函數(shù),然后將其篩選后構(gòu)造多通道進行獨立分量分析,對未知混合信號進行分選。經(jīng)過仿真驗證,這種方法擁有較好的分選效果。
[Abstract]:Radar signal sorting is to separate multiple intermingled radar pulse signals into the same emitter category. As the electromagnetic environment becomes more and more complicated, a large number of emitter signals cause pulse aliasing, the parameters are highly correlated, and the modulation methods are complex and diverse. Traditional methods which rely on several conventional signal parameters for sorting have gradually become unable to adapt to the current situation. In this paper, a method of radar signal sorting based on independent component analysis (ICA) is studied. The advantage of this method is that the signals belonging to different sources can be sorted out only if the sources are statistically independent. The main work of this paper is as follows: the traditional signal sorting method is studied, and the limitations of the traditional algorithm in the modern complex electromagnetic environment are analyzed as well as the reasons for its limitations. This paper analyzes the cause and principle of blind source separation algorithm, and introduces the fast independent component analysis algorithm based on negative entropy into signal sorting. Firstly, the intercepted signal is pretreated with normalization and whitening, then separated by independent component analysis (ICA), the parameters of the separated signal are estimated and the parameters of different time periods are classified to achieve the purpose of sorting. The simulation results show that this method has good performance. At the same time, comparing the fast independent component analysis sorting algorithm with the traditional sorting algorithm, the advantages of the fast independent component analysis sorting algorithm are confirmed from various aspects. Multi-channel receiver is not always able to satisfy the ideal positive definite problem that the signal receiving channel is larger than the number of sources. In this paper, we also propose a method of combining empirical mode decomposition with independent component analysis in the case of single channel and applying it to signal sorting. The multi-domain mixed received signal is decomposed into multiple eigenmode functions by empirical mode decomposition algorithm, and then the multi-channel independent component analysis is constructed after its filtering, and the unknown mixed signal is sorted. The simulation results show that this method has better sorting effect.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.51

【共引文獻】

相關(guān)博士學(xué)位論文 前7條

1 王法松;盲源分離的擴展模型與算法研究[D];西安電子科技大學(xué);2013年

2 王坤朋;微弱信號檢測的盲源分離方法及應(yīng)用研究[D];重慶大學(xué);2014年

3 周昊;基于盲源分離的風(fēng)力發(fā)電機主軸承振聲診斷研究[D];沈陽工業(yè)大學(xué);2014年

4 崔立志;HPLC-DAD數(shù)據(jù)分離模型及其求解算法研究[D];華東理工大學(xué);2015年

5 崔紅巖;術(shù)中脊髓監(jiān)護體感誘發(fā)電位異常預(yù)警動態(tài)預(yù)測模型研究[D];北京協(xié)和醫(yī)學(xué)院;2015年

6 李U，

本文編號：2376592