本文選題：信號分選 + 改進(jìn)PRI算法��； 參考：《上海應(yīng)用技術(shù)大學(xué)》2017年碩士論文

【摘要】：隨著雷達(dá)信號調(diào)制技術(shù)的不斷發(fā)展,輻射源的工作體制不斷變化,信號分選壞境變得日益復(fù)雜,分選的難度也就不斷增加。本文對復(fù)雜環(huán)境下未知輻射源信號的分選展開研究,注意到單一算法分選效果不理想的缺點,聯(lián)合不同類型的算法進(jìn)行信號分選。首先,分別從信號的頻域、時域、空域三組參數(shù)出發(fā)分析信號的脈間信號特征,其中重點介紹時域參數(shù)及其主要的幾種調(diào)制方式。詳細(xì)介紹幾種不同調(diào)制類型的輻射源信號,使用Matlab對信號源進(jìn)行仿真,構(gòu)建信號分選模型。簡述傳統(tǒng)PRI算法的工作流程,重點介紹PRI變換。對于大抖動下PRI變換算法存在的問題進(jìn)行改進(jìn),介紹改進(jìn)后的算法并進(jìn)行仿真實驗。通過觀察各算法實驗結(jié)果,判斷各自優(yōu)缺點。其次,簡述盲信號分離及ICA (獨立分量分析)算法的基本工作原理,重點介紹基于負(fù)熵最大化的FastICA算法,將此算法應(yīng)用到信號分選領(lǐng)域。實驗證明,此算法對不同調(diào)制類型的信號分選效果顯著,但是對含噪信號較敏感。因此,運用小波去噪算法對信號進(jìn)行去噪。綜合兩種算法進(jìn)行實驗仿真,可得出結(jié)論:基于FastICA和小波去噪的綜合算法,在較低信噪比的情況下,信號分選效果較好。最后,對于復(fù)雜環(huán)境下信號的分選,本章給出了一種新的信號分選算法,此算法綜合了正弦波抽取特性和改進(jìn)后的PRI變換這兩個算法。首先基于正弦波抽取特性的算法對信號進(jìn)行調(diào)制類型的分類,在輻射源脈沖數(shù)減少的情況下,再使用改進(jìn)后的PRI算法對相同類型下不同參數(shù)的信號進(jìn)一步的分選。仿真實驗表明此綜合算法精準(zhǔn)有效、性能較高。
[Abstract]:With the continuous development of radar signal modulation technology, the working system of emitter is constantly changing, the bad condition of signal sorting becomes more and more complex, and the difficulty of sorting becomes more and more difficult. In this paper, the signal sorting of unknown emitter in complex environment is studied, and the shortcoming of single algorithm is noticed, which combines different algorithms for signal sorting. Firstly, the characteristics of interpulse signal are analyzed from three groups of parameters in frequency domain, time domain and spatial domain, respectively, in which time domain parameters and their main modulation modes are introduced. Several emitter signals of different modulation types are introduced in detail. The signal source is simulated by Matlab and the signal sorting model is constructed. The workflow of the traditional PRI algorithm is briefly described, and the PRI transformation is emphasized. The problem of PRI transform algorithm under large jitter is improved. The improved algorithm is introduced and simulated. By observing the experimental results of each algorithm, the merits and demerits of each algorithm are judged. Secondly, the basic principle of blind signal separation and ICA (Independent component Analysis) algorithm is briefly introduced. The FastICA algorithm based on negative entropy maximization is introduced, and the algorithm is applied to the field of signal sorting. Experimental results show that the algorithm is effective for signal sorting with different modulation types, but sensitive to noisy signals. Therefore, wavelet denoising algorithm is used to denoise the signal. By synthesizing the two algorithms for experimental simulation, it can be concluded that the synthetic algorithm based on FastICA and wavelet denoising has better signal sorting effect under the condition of low signal-to-noise ratio (SNR). Finally, for the signal sorting in complex environment, a new signal sorting algorithm is presented in this chapter, which combines the sinusoidal wave extraction characteristics and the improved PRI transform. Firstly, the modulation type of signals is classified based on the algorithm of sinusoidal extraction, and then the improved PRI algorithm is used to further separate the signals with different parameters under the same type when the number of emitter pulses is reduced. The simulation results show that the algorithm is accurate and effective.
【學(xué)位授予單位】：上海應(yīng)用技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.51

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