复杂动态信号的参数估计研究

发布时间：2018-07-10 09:04

本文选题：复杂动态信号 + 参数估计　；参考：《重庆邮电大学》2016年硕士论文

【摘要】：随着通信技术不断进步,数据可靠、快速传输的重要性愈加凸显。对于无线传输方式而言,传统的窄带传输方式在抗干扰性、保密性和可靠性等方面表现出不足。而现代雷达通信要求传输的信号具有复杂波形和大带宽以提高传输的隐蔽性。为了满足这种需求,此类无线通信中采用的信号模型调制方式复杂、具有较高的动态性,因而保证了复杂环境下顺利通信。复杂动态信号待估计的参数较多、隐蔽性很强,现有方法难以直接获得其参数估计,如何在非合作的通信环境下,对复杂动态信号的参数进行估计是亟待解决的问题。因此,本文对此类调制方式复杂、具有较高动态性信号的参数与伪码估计难题进行了研究,主要针对的复杂动态信号包括正弦调频(Sinusoidal Frequency Modulation,SFM)信号、二次调频-伪码调相复合(reconnaissance signal combined with Quadratic Frequency Modulation and Pseudo-Random Binary phase Code,QFM-PRBC)信号、线性调频-伪码调相复合(reconnaissance signal combined with Linear Frequency Modulation and Pseudo-Random Binary phase Code,LFM-PRBC)信号,具体来说包括以下几点:(1)主要介绍了复杂动态信号的数学模型包括SFM信号、线性调频信号干扰下多分量SFM信号、QFM-PRBC复合信号、LFM-PRBC复合信号,分别给出了各信号的时域波形、频域波形及其它特性,还介绍了相关传统处理方法。(2)针对高斯白噪声环境下单通道多分量SFM信号参数估计的难题,研究了基于脉冲重复间隔(Pulse Repetition Internal,PRI)变换的算法。首先利用PRI变换获得混合信号中SFM分量个数以及其调制频率,然后改进离散SFM基函数,将混合信号与离散SFM基函数相乘后通过FFT变换,通过最大峰值搜索获得分量信号的载波频率、调制系数的估计,最后通过重构分量信号与混合信号相乘,获得相应分量幅度估计。(3)针对单通道线性调频信号(Linear Frequency Modulation,LFM)干扰下多分量SFM信号参数估计的难题,研究了基于脉冲重复间隔变换与中值滤波法相结合的算法。首先对混合信号进行FFT变换,经频域累加,通过中值滤波前后频谱差获得SFM信号频谱,进而利用PRI转换获得混合信号中SFM分量个数以及其调制频率,然后改进离散SFM基函数,将混合信号分解在基函数上,进行FFT变换后通过峰值搜索获得分量信号的载波频率、调制系数的估计,最后通过重构相应分量信号与混合信号相乘,获得相应分量幅度估计。(4)针对QFM-PRBC复合信号伪码盲估计的难题,研究了分数阶傅里叶变换的模糊函数和改进基于三角窗抑制干扰核函数分布相结合的算法。在对接收信号进行平方处理后,应用累加平均降低噪声的干扰,采用分数阶傅里叶变换的模糊函数估计出二、三阶系数,然后重构信号对接收信号降阶,使用奇异值分解对基于三角窗抑制干扰核函数分布进行改进,优化其呈现的时频图,进而提取到相应伪码序列。(5)针对多分量LFM-PRBC复合信号伪码盲估计的难题,研究了线性正则变换处理的方法。在对接收信号进行平方处理后,应用累加平均降低噪声的干扰,采用线性正则变换估计一、二阶系数,然后重构信号对接收信号降阶,得到相应分量幅度估计以及相应伪码序列。消除已估分量的影响,再进行迭代操作,直至获得所有分量的参数估计。上述所介绍的信号模型和相关算法都进行了相应的计算机仿真实验分析和说明,验证了算法的可行性和有效性,并在一定的信噪比条件下,具有不错的估计性能。
[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.
【学位授予单位】：重庆邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN911.23

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