發(fā)布時(shí)間：2018-04-22 20:04

  本文選題：脈沖信號(hào) + 分選��； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：脈沖信號(hào)分選的任務(wù)就是將偵察系統(tǒng)接收到的脈沖信號(hào)中屬于同一輻射源的脈沖分離,以備對(duì)輻射源進(jìn)行識(shí)別和進(jìn)一步信號(hào)處理。對(duì)脈沖信號(hào)進(jìn)行有效而快速的分選,是電子偵察系統(tǒng)中的關(guān)鍵一環(huán),各國(guó)研究人員也非常重視脈沖分選技術(shù)的研究,并取得了一定的成果。傳統(tǒng)的脈沖信號(hào)分選以脈間參數(shù)即脈沖描述字PDW作為分選特征,在固定容差下分組作為預(yù)處理以稀釋脈沖密度,再利用脈沖到達(dá)時(shí)間(TOA)參數(shù)對(duì)脈沖串的脈沖重復(fù)間隔(PRI)進(jìn)行估計(jì)并去交錯(cuò)。傳統(tǒng)分選方法計(jì)算簡(jiǎn)單,易于實(shí)現(xiàn),能對(duì)常規(guī)體制的信號(hào)進(jìn)行有效的分選,在實(shí)際工程應(yīng)用中被廣泛采用。隨著當(dāng)前信號(hào)環(huán)境復(fù)雜化,脈間參數(shù)多變,參數(shù)空間交疊嚴(yán)重,傳統(tǒng)分選方法對(duì)于截獲的非合作復(fù)雜脈沖信號(hào)的分選效果惡化,迫切需要分析和利用脈沖更多的信息進(jìn)行分選。本文主要對(duì)非合作復(fù)雜脈沖信號(hào)分選技術(shù)進(jìn)行研究和實(shí)踐,主要內(nèi)容如下:1.在分析當(dāng)前復(fù)雜信號(hào)類型的基礎(chǔ)上,總結(jié)了傳統(tǒng)脈沖分選系統(tǒng)結(jié)構(gòu)的不足,并建立了適用于復(fù)雜信號(hào)環(huán)境的改進(jìn)分選結(jié)構(gòu)框架,并結(jié)合新的分選框架展開對(duì)脈間參數(shù)分選、脈內(nèi)特征提取和聚類分選三個(gè)方面的技術(shù)研究。2.對(duì)三種經(jīng)典的脈間參數(shù)分選方法——CDIF、SDIF與PRI變換法進(jìn)行了深入分析,仿真結(jié)果表明脈間參數(shù)分選方法能實(shí)現(xiàn)對(duì)固定PRI的脈沖信號(hào)的分選。本文在此基礎(chǔ)上改進(jìn)了PRI變換法以適應(yīng)PRI抖動(dòng)信號(hào)分選,并驗(yàn)證改進(jìn)后方法對(duì)復(fù)雜信號(hào)環(huán)境下的脈沖分選取得了良好效果。3.從脈內(nèi)有意調(diào)制特征入手,通過進(jìn)行小波變換和模糊函數(shù)分析提取可分性好的脈內(nèi)特征用于分選。對(duì)基于相位信息的小波脊提取迭代算法進(jìn)行改進(jìn),并提出模糊函數(shù)特征的改進(jìn)快速提取方法,提高了特征提取的速度并保留了良好的抗噪性能,經(jīng)仿真驗(yàn)證了小波脊提取信號(hào)瞬時(shí)頻率特征和模糊函數(shù)特征能有效實(shí)現(xiàn)不同脈內(nèi)調(diào)制方式的脈沖信號(hào)分選。4.對(duì)聚類分選方法中的改進(jìn)FCM算法進(jìn)行研究,比較其與傳統(tǒng)C均值聚類算法的優(yōu)勢(shì)。在分析總結(jié)傳統(tǒng)C均值聚類與FCM算法的基礎(chǔ)上,引入SAKM算法的分析,通過仿真試驗(yàn)表明SAKM算法實(shí)時(shí)性更強(qiáng)、適應(yīng)性更好,更適合對(duì)未知輻射源信號(hào)的分選,有較大的發(fā)展空間。
[Abstract]:The task of pulse signal sorting is to separate the pulse which belongs to the same emitter in the pulse signal received by the reconnaissance system for identification and further signal processing of the emitter. The effective and fast sorting of pulse signals is a key link in electronic reconnaissance system. Researchers from all over the world also attach great importance to the research of pulse sorting technology and have achieved certain results. In the traditional pulse signal sorting, the pulse description word PDW is used as the sorting feature, and the pulse density is diluted by preprocessing under the fixed tolerance. Then the pulse repetition interval (PRI) of the pulse train is estimated and deinterleaved with the pulse arrival time (toa) parameter. The traditional sorting method is simple in calculation and easy to realize. It can effectively separate signals from conventional systems and is widely used in practical engineering applications. With the complexity of the current signal environment, the variable parameters between the pulse and the serious overlap of the parameters, the traditional sorting method for the intercepted non-cooperative complex pulse signal is deteriorating, so it is urgent to analyze and use more information of the pulse for sorting. This paper focuses on the research and practice of noncooperative complex pulse signal sorting technology, the main contents are as follows: 1. Based on the analysis of the current complex signal types, the shortcomings of the traditional pulse sorting system structure are summarized, and the improved sorting structure framework suitable for the complex signal environment is established, and the inter-pulse parameter sorting is carried out in combination with the new separation framework. Research on Intra-pulse feature extraction and clustering and sorting. Three classical inter-pulse parameter sorting methods, CDIF-SDIF and PRI transform, are deeply analyzed. The simulation results show that the inter-pulse parameter sorting method can realize the separation of pulse signals of fixed PRI. In this paper, the PRI transform method is improved to adapt to the PRI jitter signal sorting, and it is verified that the improved method has a good effect on pulse sorting in complex signal environment. Based on the feature of intrapulse intentional modulation, wavelet transform and ambiguity function analysis are used to extract good divisibility features for sorting. The iterative algorithm of wavelet ridge extraction based on phase information is improved, and an improved and fast method for feature extraction of fuzzy function is proposed, which improves the speed of feature extraction and retains good anti-noise performance. The simulation results show that the wavelet ridges extract the instantaneous frequency feature and the ambiguity function feature can effectively realize the pulse signal sorting with different pulse modulation modes. 4. This paper studies the improved FCM algorithm in clustering and sorting method, and compares its advantages with the traditional C-means clustering algorithm. On the basis of analyzing and summarizing the traditional C-means clustering and FCM algorithm, the analysis of SAKM algorithm is introduced. The simulation results show that the SAKM algorithm is more real-time, more adaptable, more suitable for the sorting of unknown emitter signals, and has a large development space.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.51

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本文編號(hào)：1788719