基于多项式相位信号的参数估计算法研究

发布时间：2018-05-05 05:23

本文选题：多相位信号 + 相位参数估计　；参考：《电子科技大学》2016年硕士论文

【摘要】：多相位信号广泛应用于当代的雷达、通信、生物医学领域、地震勘测和声呐系统中。因此,基于噪声干扰下多项式相位信号的时频分析与参数估计问题也成为信号处理领域的研究热点之一。经过近几十年的研究,基于单分量、多分量的多项式相位信号已经形成了较为完善的时频分析和参数估计理论。随着研究的深入,传统的算法已无法满足实际应用与研究中对参数估计高精度需求,同时,传统方法也面临着处理多分量信号过程中呈现出的交错项干扰、信号间串扰等问题。因此,如何抑制多分量信号干扰并提高参数估计性能成为了信号参数估计的重点研究对象。本文对此作了如下工作:1.基于单分量多相位信号提出了乘积性三次相位函数拓展方法,利用乘积性方法对噪声干扰的抑制性,实现三次相位信号在低信噪比下的参数估计,同时提高参数估计精度。2.研究并通过仿真验证了三次相位函数拓展方法在估计多分量多相位信号时的局限性;分析了乘积性三次相位函数拓展方法在抑制三次相位函数拓展方法下交错项干扰上的特性,及其在提高参数估计的精度上的优势,指出了该方法在参数匹配上的不足;最后,综合上述问题,提出了一种基于乘积性非线性变换的三阶相位信号参数估计与检测方法,采用乘积性方法增强信号自项并弱化交叉项,利用依次滤波方法实现信号分离,从而解决了三次相位函数拓展方法与乘积性三次相位函数拓展方法在多分量多相位信号参数估计中的缺陷,提高低信噪比下的参数估计性能。3.研究并证实了三次相位函数随机变换方法、乘积性混合非线性变换方法在估计参数相近混合信号时的局限性,提出了组合三次相位函数随机变换方法来抑制前两种方法下的信号间串扰,提高了在低信噪比条件下混合多相位信号的参数估计性能。4.结合捷变频仿真信号和捷变频非合作雷达实测信号给出了乘积性三次相位函数方法下重构并抑制直达波信号的时频分析图,通过信号抑制前后的时域对比图证实了该方法在重构信号上的实用性。
[Abstract]:Polyphase signals are widely used in modern radar, communication, biomedical, seismic survey and sonar systems. Therefore, the time-frequency analysis and parameter estimation of polynomial phase signals based on noise interference have become one of the research hotspots in the field of signal processing. After decades of research, the polynomial phase signal based on single component and multi-component has formed a more perfect theory of time-frequency analysis and parameter estimation. With the development of the research, the traditional algorithm can not meet the requirement of high precision parameter estimation in practical application and research. At the same time, the traditional method is faced with the problems of interlaced interference and inter-signal crosstalk in the process of dealing with multi-component signals. Therefore, how to suppress multi-component signal interference and improve the performance of parameter estimation has become the focus of research on signal parameter estimation. This paper does the following work: 1. Based on the single-component multi-phase signal, a new method of expanding the product cubic phase function is proposed. By using the product method to suppress the noise interference, the parameter estimation of the cubic phase signal at low signal-to-noise ratio is realized, and the precision of parameter estimation is improved at the same time. The limitation of cubic phase function expansion method in estimating multi-component multi-phase signals is studied and verified by simulation, and the characteristics of product cubic phase function expansion method in suppressing interlaced interference under cubic phase function expansion method are analyzed. And its advantages in improving the accuracy of parameter estimation, pointing out the shortcomings of the method in parameter matching. Finally, a method for parameter estimation and detection of third-order phase signals based on product nonlinear transformation is proposed. The product method is used to enhance the signal self-term and weaken the crossover term, and the sequential filtering method is used to separate the signal. Thus, the defects of cubic phase function expansion method and product cubic phase function expansion method in multi-component multi-phase signal parameter estimation are solved, and the performance of parameter estimation under low signal-to-noise ratio is improved. The limitations of the cubic phase function random transformation method and the product mixed nonlinear transformation method in estimating the mixed signals with similar parameters are studied and verified. A combined cubic phase function random transformation method is proposed to suppress the crosstalk between the first two methods and improve the parameter estimation performance of mixed multi-phase signals under low signal-to-noise ratio (SNR). The time-frequency analysis diagram of reconstruction and suppression of direct wave signal under the method of product cubic phase function is given by combining agile frequency simulation signal and measured signal of agile non-cooperative radar. The practicability of this method in reconstruction signal is proved by time domain contrast diagram before and after signal suppression.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN911.7

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