本文選題：復(fù)雜動(dòng)態(tài)信號(hào) + 參數(shù)估計(jì)��； 參考：《重慶郵電大學(xué)》2016年碩士論文

【摘要】：隨著通信技術(shù)不斷進(jìn)步,數(shù)據(jù)可靠、快速傳輸?shù)闹匾杂油癸@。對(duì)于無(wú)線傳輸方式而言,傳統(tǒng)的窄帶傳輸方式在抗干擾性、保密性和可靠性等方面表現(xiàn)出不足。而現(xiàn)代雷達(dá)通信要求傳輸?shù)男盘?hào)具有復(fù)雜波形和大帶寬以提高傳輸?shù)碾[蔽性。為了滿足這種需求,此類無(wú)線通信中采用的信號(hào)模型調(diào)制方式復(fù)雜、具有較高的動(dòng)態(tài)性,因而保證了復(fù)雜環(huán)境下順利通信。復(fù)雜動(dòng)態(tài)信號(hào)待估計(jì)的參數(shù)較多、隱蔽性很強(qiáng),現(xiàn)有方法難以直接獲得其參數(shù)估計(jì),如何在非合作的通信環(huán)境下,對(duì)復(fù)雜動(dòng)態(tài)信號(hào)的參數(shù)進(jìn)行估計(jì)是亟待解決的問(wèn)題。因此,本文對(duì)此類調(diào)制方式復(fù)雜、具有較高動(dòng)態(tài)性信號(hào)的參數(shù)與偽碼估計(jì)難題進(jìn)行了研究,主要針對(duì)的復(fù)雜動(dòng)態(tài)信號(hào)包括正弦調(diào)頻(Sinusoidal Frequency Modulation,SFM)信號(hào)、二次調(diào)頻-偽碼調(diào)相復(fù)合(reconnaissance signal combined with Quadratic Frequency Modulation and Pseudo-Random Binary phase Code,QFM-PRBC)信號(hào)、線性調(diào)頻-偽碼調(diào)相復(fù)合(reconnaissance signal combined with Linear Frequency Modulation and Pseudo-Random Binary phase Code,LFM-PRBC)信號(hào),具體來(lái)說(shuō)包括以下幾點(diǎn):(1)主要介紹了復(fù)雜動(dòng)態(tài)信號(hào)的數(shù)學(xué)模型包括SFM信號(hào)、線性調(diào)頻信號(hào)干擾下多分量SFM信號(hào)、QFM-PRBC復(fù)合信號(hào)、LFM-PRBC復(fù)合信號(hào),分別給出了各信號(hào)的時(shí)域波形、頻域波形及其它特性,還介紹了相關(guān)傳統(tǒng)處理方法。(2)針對(duì)高斯白噪聲環(huán)境下單通道多分量SFM信號(hào)參數(shù)估計(jì)的難題,研究了基于脈沖重復(fù)間隔(Pulse Repetition Internal,PRI)變換的算法。首先利用PRI變換獲得混合信號(hào)中SFM分量個(gè)數(shù)以及其調(diào)制頻率,然后改進(jìn)離散SFM基函數(shù),將混合信號(hào)與離散SFM基函數(shù)相乘后通過(guò)FFT變換,通過(guò)最大峰值搜索獲得分量信號(hào)的載波頻率、調(diào)制系數(shù)的估計(jì),最后通過(guò)重構(gòu)分量信號(hào)與混合信號(hào)相乘,獲得相應(yīng)分量幅度估計(jì)。(3)針對(duì)單通道線性調(diào)頻信號(hào)(Linear Frequency Modulation,LFM)干擾下多分量SFM信號(hào)參數(shù)估計(jì)的難題,研究了基于脈沖重復(fù)間隔變換與中值濾波法相結(jié)合的算法。首先對(duì)混合信號(hào)進(jìn)行FFT變換,經(jīng)頻域累加,通過(guò)中值濾波前后頻譜差獲得SFM信號(hào)頻譜,進(jìn)而利用PRI轉(zhuǎn)換獲得混合信號(hào)中SFM分量個(gè)數(shù)以及其調(diào)制頻率,然后改進(jìn)離散SFM基函數(shù),將混合信號(hào)分解在基函數(shù)上,進(jìn)行FFT變換后通過(guò)峰值搜索獲得分量信號(hào)的載波頻率、調(diào)制系數(shù)的估計(jì),最后通過(guò)重構(gòu)相應(yīng)分量信號(hào)與混合信號(hào)相乘,獲得相應(yīng)分量幅度估計(jì)。(4)針對(duì)QFM-PRBC復(fù)合信號(hào)偽碼盲估計(jì)的難題,研究了分?jǐn)?shù)階傅里葉變換的模糊函數(shù)和改進(jìn)基于三角窗抑制干擾核函數(shù)分布相結(jié)合的算法。在對(duì)接收信號(hào)進(jìn)行平方處理后,應(yīng)用累加平均降低噪聲的干擾,采用分?jǐn)?shù)階傅里葉變換的模糊函數(shù)估計(jì)出二、三階系數(shù),然后重構(gòu)信號(hào)對(duì)接收信號(hào)降階,使用奇異值分解對(duì)基于三角窗抑制干擾核函數(shù)分布進(jìn)行改進(jìn),優(yōu)化其呈現(xiàn)的時(shí)頻圖,進(jìn)而提取到相應(yīng)偽碼序列。(5)針對(duì)多分量LFM-PRBC復(fù)合信號(hào)偽碼盲估計(jì)的難題,研究了線性正則變換處理的方法。在對(duì)接收信號(hào)進(jìn)行平方處理后,應(yīng)用累加平均降低噪聲的干擾,采用線性正則變換估計(jì)一、二階系數(shù),然后重構(gòu)信號(hào)對(duì)接收信號(hào)降階,得到相應(yīng)分量幅度估計(jì)以及相應(yīng)偽碼序列。消除已估分量的影響,再進(jìn)行迭代操作,直至獲得所有分量的參數(shù)估計(jì)。上述所介紹的信號(hào)模型和相關(guān)算法都進(jìn)行了相應(yīng)的計(jì)算機(jī)仿真實(shí)驗(yàn)分析和說(shuō)明,驗(yàn)證了算法的可行性和有效性,并在一定的信噪比條件下,具有不錯(cuò)的估計(jì)性能。
[Abstract]:As the communication technology is progressing, the data is reliable and the importance of rapid transmission is becoming more and more important. For wireless transmission, the traditional narrow band transmission is insufficient in the aspects of anti-jamming, security and reliability. And modern radar communication requires complex waveforms and large bandwidth to improve the concealment of transmission. In order to meet this demand, the modulation mode of the signal model used in this kind of wireless communication is complex and has high dynamic character, thus ensuring the smooth communication in the complex environment. The complex dynamic signal has many parameters to be estimated and has strong concealment. It is difficult to obtain the parameter estimation directly by the existing method, and how to be in the non cooperative communication environment. It is an urgent problem to estimate the parameters of complex dynamic signals. Therefore, this kind of modulation is complicated, the parameter of high dynamic signal and the pseudo code estimation problem are studied. The complex dynamic signals mainly include the sinusoidal frequency modulation (Sinusoidal Frequency Modulation, SFM) signal and the two frequency modulation pseudo code modulation. Reconnaissance signal combined with Quadratic Frequency Modulation and Pseudo-Random Binary phase Code signal, linear frequency modulation - pseudo code phase modulation composite signal, specifically included The next points are as follows: (1) the mathematical models of complex dynamic signals are mainly introduced, including SFM signal, multicomponent SFM signal, QFM-PRBC compound signal and LFM-PRBC compound signal under the interference of linear frequency modulation signal. The time domain waveform, frequency domain waveform and other characteristics of each signal are given respectively, and the related traditional processing methods are introduced. (2) Gauss white noise ring is introduced. The problem of parameter estimation of single channel and multicomponent SFM signals is studied. The algorithm based on the Pulse Repetition Internal (PRI) transformation is studied. First, the number of SFM components and its modulation frequency in the mixed signal are obtained by PRI transform, then the discrete SFM base function is improved, and the mixed signal is multiplied with the discrete SFM base function through FFT. Through the maximum peak search, the carrier frequency of the component signal is obtained, the modulation coefficient is estimated. Finally, the amplitude estimation of the corresponding component is obtained by multiplying the reconstructed component signal to the mixed signal. (3) the problem of the parameter estimation of the multicomponent SFM signal under the interference of the single channel linear frequency modulation signal (Linear Frequency Modulation, LFM) is studied. Based on the combination of pulse repetition interval transformation and median filtering, the mixed signal is transformed by FFT transform, and the frequency spectrum is added through the frequency domain to obtain the SFM signal spectrum through the spectrum difference before and after the median filter, and then the number of SFM components and its modulation frequency in the mixed signal are obtained by PRI conversion, and then the discrete SFM base function is improved and the mixed letter is used. The number is decomposed on the base function, after the FFT transformation, the carrier frequency of the component signal is obtained by the peak search, the modulation coefficient is estimated. Finally, the corresponding component amplitude is obtained by multiplying the corresponding component signal to the mixed signal. (4) the modulus of the fractional Fourier transform is studied for the problem of the blind code blind estimation of the QFM-PRBC composite signal. The algorithm combined with the distribution of the interference kernel function based on the triangle window is improved. After the square processing of the received signal, the two, the three order coefficients are estimated by the fuzzy function of fractional Fourier transform, and then the signal is reconstructed by the fuzzy function of the fractional Fourier transform, and the signal is reconstructed by the singular value decomposition of the base. In the triangle window, the distribution of interference kernel function is improved, its time frequency graph is optimized, and then the corresponding pseudo-code sequence is extracted. (5) in view of the problem of blind code blind estimation of multicomponent LFM-PRBC composite signal, the method of linear regular transformation processing is studied. After the square processing of the received signal, the interference of noise is reduced by accumulating average. The linear regular transformation is used to estimate the first, two order coefficients, then the reconstructed signal reduces the order of the received signal, obtains the amplitude estimation of the corresponding component and the corresponding pseudo-code sequence, eliminates the influence of the estimated component, and then performs the iterative operation until the parameter estimation of all components is obtained. The above mentioned signal model and the related algorithm are all corresponding. Computer simulation experiments show that the algorithm is feasible and effective, and has good estimation performance under certain SNR conditions.
【學(xué)位授予單位】：重慶郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN911.23

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