本文選題：線性調(diào)頻信號(hào) + 分?jǐn)?shù)階傅里葉變換　； 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：線性調(diào)頻信號(hào)(LFM)是一種雷達(dá)和通信等信息系統(tǒng)常采用的信號(hào)波形。一方面,由于現(xiàn)代電子對(duì)抗中電磁環(huán)境更加復(fù)雜多變,使對(duì)LFM信號(hào)的分選與識(shí)別變得十分棘手。對(duì)LFM信號(hào)各項(xiàng)參數(shù)的檢測(cè),特別是在信噪比并不理想的條件下,完成對(duì)LFM信號(hào)的檢測(cè)和參數(shù)估計(jì),具有相當(dāng)重要意義。另一方面,接收機(jī)會(huì)在同一時(shí)刻接收到不止一個(gè)信號(hào),因此要從混疊信號(hào)中檢測(cè)出各個(gè)信號(hào)的特征參數(shù)并將它們分離,成為電子信息對(duì)抗領(lǐng)域中一個(gè)亟待研究的重要課題。本文主要研究的是基于分?jǐn)?shù)階傅里葉變換(Fractional Fourier Transform,FrFT)的時(shí)頻分析方法,包括對(duì)信號(hào)參數(shù)估計(jì)和分離的研究。在對(duì)LFM信號(hào)進(jìn)行參數(shù)估計(jì)時(shí),傳統(tǒng)的時(shí)頻分析方法如短時(shí)傅里葉變換、Wigner-Ville分布等方法中存在的分辨率低、交叉項(xiàng)干擾等問題,相比之下,FrFT就不存在這樣的弊端。不過FrFT中涉及二維峰值搜索,搜索中涉及很大的計(jì)算量,影響對(duì)信號(hào)檢測(cè)的實(shí)時(shí)性。本文針對(duì)上述問題,首先提出了對(duì)應(yīng)的改進(jìn)算法:基于FrFT插值法的改進(jìn)算法和基于Nuttall窗的能量重心法。其中插值法的改進(jìn)算法中利用對(duì)頻譜進(jìn)行插值處理的方法,在保證參數(shù)估計(jì)精度的條件下,加大搜索步長(zhǎng)r,減小二維搜索的運(yùn)算量;而基于Nuttall窗的能量重心法則是利用Nuttall窗的優(yōu)良性能提高能量重心法估計(jì)調(diào)頻率時(shí)的精度,利用調(diào)頻率和FrFT階數(shù)的關(guān)系,在小范圍內(nèi)進(jìn)行搜索得到精確的調(diào)頻率和起始頻率。最后,對(duì)改進(jìn)算法進(jìn)行了 MATLAB軟件仿真,驗(yàn)證了其性能�；贑LEAN思想的多分量信號(hào)分離方法,采用遮蔽已知信號(hào)的方法能夠成功的將多分量LFM信號(hào)分離并且同時(shí)逐一完成參數(shù)估計(jì)。但這種分離方法無(wú)法應(yīng)用于FrFT改進(jìn)算法中的降維處理,使用受到了限制。本文介紹了一種改進(jìn)的CLEAN分離方法,使其分離算法契合FrFT改進(jìn)算法中的降維思想,同時(shí)進(jìn)行了軟件仿真。最后利用硬件系統(tǒng)對(duì)算法進(jìn)行了實(shí)驗(yàn)測(cè)試,檢驗(yàn)了算法在實(shí)驗(yàn)條件下的性能,實(shí)驗(yàn)結(jié)果與MATLAB仿真結(jié)果一致,證明了該方法的工程實(shí)用價(jià)值。
[Abstract]:Linear Frequency Modulation signal (LFM) is a kind of signal waveform commonly used in radar and communication information systems.On the one hand, the electromagnetic environment is more complex and changeable in modern electronic countermeasures, which makes the sorting and recognition of LFM signals very difficult.It is of great significance to detect and estimate the parameters of LFM signal, especially under the condition that SNR is not ideal.On the other hand, the receiver will receive more than one signal at the same time, so it is necessary to detect and separate the characteristic parameters of each signal from the aliasing signal, which is an important subject to be studied in the field of electronic information countermeasure.This paper mainly studies the time-frequency analysis method based on Fractional Fourier transform (FrFT), including the estimation and separation of signal parameters.In parameter estimation of LFM signal, the traditional time-frequency analysis methods such as short time Fourier transform (STFT) Wigner-Ville distribution have some problems such as low resolution, cross term interference and so on.However, 2D peak search is involved in FrFT, which involves a lot of computation, which affects the real time of signal detection.In order to solve the above problems, this paper first proposes the corresponding improved algorithm: the improved algorithm based on FrFT interpolation and the energy barycenter method based on Nuttall window.In the improved interpolation algorithm, the interpolation method is used to interpolate the spectrum. Under the condition of guaranteeing the precision of parameter estimation, the search step size is increased, and the operation amount of two-dimensional search is reduced.The energy barycenter rule based on Nuttall window is to improve the accuracy of the energy barycenter method when estimating the frequency by using the excellent performance of the Nuttall window. By using the relation between the modulation frequency and the FrFT order, the accurate tuning frequency and the starting frequency can be obtained by searching in a small range.Finally, the improved algorithm is simulated by MATLAB software and its performance is verified.The multi-component signal separation method based on the idea of CLEAN can successfully separate the multi-component LFM signal and complete the parameter estimation one by one by using the method of masking the known signal.However, this separation method can not be applied to the dimensionality reduction in the improved FrFT algorithm, and its use is limited.In this paper, an improved CLEAN separation method is introduced to fit the dimensionality reduction idea of the improved FrFT algorithm, and the software simulation is carried out at the same time.Finally, the algorithm is tested by hardware system, and the performance of the algorithm under the experimental condition is tested. The experimental results are in agreement with the results of MATLAB simulation, and the practical value of the method is proved.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.23

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