航天测控链路干扰信号感知与特征参数估计技术研究

发布时间：2018-04-15 10:32

本文选题：深度学习 + 卷积神经网络　；参考：《哈尔滨工业大学》2017年硕士论文

【摘要】：航天测控在军事领域的应用非常广泛,卫星对抗成为空间信息侦查与对抗的主要领域。在这个基础上,研究航天测控链路的干扰感知、自动识别具有非常重要意义。经过多年的发展,干扰信号的感知和识别已经取得了很多的成果,但是大多数的方法只能针对特定的干扰类型进行检测,具有很大的局限性。同时,大多数的干扰类型识别的特征提取使用的是传统的模式识别方法,需要人工进行干扰信号的特征的提取,这需要消耗很大的工作量。本文从能量检测算法和深度学习网络着手,研究干扰信号的检测、特征识别和参数估计的机理及实现方法。本文首先对有涉及到的5种需要感知识别的干扰信号进行了分析说明,5种干扰信号包括音频干扰、同频带窄带干扰、扫频干扰、矩形脉冲干扰以及扩频干扰。之后,通过对能量检测算法和恒虚警检测算法的研究,成功使用能量检测算法对干扰信号进行了存在性的检测,实验表明干扰信号在干噪比较高的环境下取得了很好的成果,但是在干噪比较低的环境下,能量检测算法的性能急剧下降。因此,本文提出了使用深度学习网络和能量检测算法对干扰信号进行联合检测的方法。实验结果表明,基于卷积神经网络的干扰检测算法在干噪比很低的环境下检测性能优于能量检测算法。然后使用了深度学习网络对5种干扰信号进行了特征的提取,使用多维尺度分析方法对提取出的特征信息进行了分析,结果表明提取出的特征具有明显的可分性与鲁棒性,接着将提取出了干扰信号特征送入Softmax分类器进行分类。实验结果该种分类方法在干噪比为-5dB~15dB时对单一干扰信号的分类正确率几乎达到了100%,而并存干扰的分类正确率达到了99%以上。最后,利用四阶统计特性代替原能量检测器的平方特性,在感知到对方干扰卫星通信后,利用“窗”的思想,采用第二类切比雪夫滤波器估计窄带干扰信号的中心频率和宽带干扰信号的带宽。实验结果,在干噪比为-5dB~15d B的环境下干扰信号参数估计的准确率在96%以上。
[Abstract]:Space TT & C is widely used in military field, satellite countermeasure becomes the main field of space information detection and countermeasure.On this basis, it is very important to study the interference perception and automatic recognition of space TT & C link.After years of development, many achievements have been made in the perception and recognition of interference signals, but most of the methods can only be detected for specific types of interference, which has great limitations.At the same time, most of the feature extraction of interference type recognition uses the traditional pattern recognition method, which needs to extract the feature of interference signal manually, which needs a lot of work.In this paper, energy detection algorithm and depth learning network are used to study the mechanism and implementation of interference signal detection, feature identification and parameter estimation.In this paper, firstly, five kinds of interference signals which need to be sensed and identified are analyzed. The five kinds of interference signals include audio interference, narrowband interference in the same frequency band, sweep interference, rectangular pulse interference and spread spectrum interference.Then, through the research of the energy detection algorithm and the constant false alarm detection algorithm, the existence of the interference signal is detected successfully by using the energy detection algorithm. The experiment shows that the interference signal has achieved good results in the environment of high dry noise.But in the environment of low dry noise, the performance of the energy detection algorithm drops sharply.Therefore, a method of joint detection of interference signals using depth learning network and energy detection algorithm is proposed.Experimental results show that the detection performance of the interference detection algorithm based on convolution neural network is better than that of the energy detection algorithm under the condition of low dry-noise ratio.Then, the features of five kinds of interference signals are extracted by using the deep learning network, and the feature information is analyzed by using multidimensional scale analysis method. The results show that the extracted features have obvious separability and robustness.Then the feature of the interference signal is extracted and sent into the Softmax classifier for classification.Experimental results show that the classification accuracy of single interference signal is almost 100 when the dry noise ratio is -5 dB, and the classification accuracy rate with interference is more than 99%.Finally, the fourth-order statistical characteristic is used instead of the square characteristic of the original energy detector. After sensing the interference of the other side to the satellite communication, the idea of "window" is used.The second kind of Chebyshev filter is used to estimate the center frequency of narrowband interference signal and the bandwidth of wideband interference signal.The experimental results show that the estimation accuracy of interference signal parameters is over 96% under the environment of -5 dB / 15dB.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN97;V556

【参考文献】