基于混沌理论的弱信号检测方法的研究

发布时间：2019-02-18 21:00

【摘要】：弱信号的检测在通信、雷达等领域有着广泛的应用,而在强噪声背景下检测微弱信号是近代信息理论的一个重要的研究热点,并促使人们不断探索与研究弱信号检测的新理论和新方法。传统的弱信号时域检测方法,往往受到信噪比门限的限制,而近年来随着人们对非线性科学中混沌理论的研究,为解决问题提供了一条新的思路,基于混沌理论的检测方法克服了传统方法的不足,可以检测更低信噪比的信号,为弱信号的检测提供了新的研究理论和方法。本文在分析混沌动力学系统的基础上,以霍尔姆斯型Duffing映射为研究对象,把Lyapunov指数作为混沌识别的判据,通过QR分解法求出动力学方程由混沌状态变为周期状态的混沌临界阈值,重点对利用混沌Duffing振子进行弱信号检测的基本原理和检测方法进行了深入的分析,验证了基于相轨迹变化来判断待检测信号中是否含有目标信号的检测算法的可行性,针对未知频率的信号检测,采用控制理论中的滑模变结构控制方法对霍尔姆斯型Duffimg系统进行了改进。仿真结果表明,改进的混沌Duffing系统可以有效抑制噪声,并通过系统的功率谱图检测出弱信号的频率。利用支持向量机理论、遗传算法和粒子群优化算法建立一步预测模型对混沌信号短期预测,把相空间重构参数和支持向量机模型参数同时进行组合优化,根据得到的最优参数建立预测模型,并用混沌时间序列验证了模型的准确性,同时利用混沌噪声与弱信号混合后的信号与另外两种传统的参数求取方法进行仿真和比较分析。结果表明本文所提出的的方法相比于传统的参数计算方法,在检测性能方面有了明显地提高。
[Abstract]:Weak signal detection has been widely used in communication, radar and other fields. Detection of weak signal in strong noise background is an important research hotspot in modern information theory. It also urges people to explore and study new theory and method of weak signal detection. Traditional weak signal detection methods in time domain are often limited by the signal-to-noise ratio (SNR) threshold. In recent years, with the study of chaos theory in nonlinear science, a new way of thinking is provided for solving the problem. The detection method based on chaos theory overcomes the shortcomings of traditional methods and can detect lower signal-to-noise ratio (SNR) signals, which provides a new theory and method for weak signal detection. In this paper, based on the analysis of chaotic dynamical system, the Lyapunov exponent is taken as the criterion of chaos recognition by taking the Duffing map of Holmes type as the research object. The critical threshold of dynamic equation from chaotic state to periodic state is obtained by QR decomposition method. The basic principle and detection method of weak signal detection using chaotic Duffing oscillator are analyzed in detail. The feasibility of the detection algorithm based on the phase locus change to judge whether the signal to be detected contains the target signal or not is verified, and the detection of the signal with unknown frequency is verified. The sliding mode variable structure control method in control theory is used to improve the Holmes type Duffimg system. The simulation results show that the improved chaotic Duffing system can effectively suppress noise and detect the frequency of weak signal by the power spectrum of the system. Based on the support vector machine theory, genetic algorithm and particle swarm optimization algorithm, a one-step prediction model is established for short-term prediction of chaotic signals. The phase space reconstruction parameters and support vector machine model parameters are combined and optimized simultaneously. According to the obtained optimal parameters, the prediction model is established, and the accuracy of the model is verified by chaotic time series. At the same time, the signals mixed with chaotic noise and weak signals are simulated and compared with the other two traditional parameters. The results show that the proposed method is better than the traditional parameter calculation method in detecting performance.
【学位授予单位】：西安科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.23

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