基于压缩传感理论的雷达信号检测方法研究

发布时间：2018-01-06 11:07

本文关键词：基于压缩传感理论的雷达信号检测方法研究　出处：《大连海事大学》2017年硕士论文　论文类型：学位论文

【摘要】：信号检测作为雷达系统中的重要问题,一直受到国内外学者的关注,主要因为信号检测不管在军用领域还是民用领域都有着广泛的应用,例如雷达侦查、船舶安全航行等。为了获得更好的抗干扰性和更高的分辨率,雷达系统经常采用大时宽带宽信号作为发射信号。但在传统的奈奎斯特采样框架下,大时宽带宽势必会带来大数据量的采集、传输、储存和处理问题。而压缩传感理论的出现恰好成为解决这一问题的有效工具。多项式相位信号(PPS)是一种常用的雷达宽带信号,因此本文将对PPS的检测问题进行研究,结合压缩传感理论,分析和实现PPS信号的检测算法。本文首先研究了 PPS信号的稀疏表示方法,然后针对不同阶次的PPS信号构造不同的稀疏字典。针对二阶PPS信号,即LFM信号,为其构造了波形延时字典和FRFT正交基字典,并通过实验验证这两种字典都能对LFM信号稀疏表示,但FRFT字典的抗白噪声干扰更强。针对三阶PPS信号,为其构造了波形匹配字典。其次研究了基于压缩传感检测模型的建立以及检测模型下压缩检测算法的设计与实现。首先建立了一种高斯白噪声信道条件下的检测模型。现有的检测方法有基于稀疏系数位置的检测算法,但其在低信噪条件下检测效果不佳,而且针对的是已知信号。于是将归一化残差引入到检测算法中。并分别根据LFM信号和三阶PPS信号在雷达信号中的不同应用,针对LFM信号,引入归一化残差检测算法,验证了此算法的有效性。然后将多脉冲检测引入到LFM信号检测中,又提出了一种多重检测算法和一种积累检测算法,经过仿真证明了这两种多脉冲检测算法相对于单脉冲的归一化残差检测算法都提高了检测性能,并且积累检测算法在性能上更具有优势。针对三阶PPS信号,重点研究了多分量模型下的归一化残差的检测算法,并根据归一化残差斜率的特性提出了一种信源个数估计算法,最后验证了此算法对信源个数估计的有效性。
[Abstract]:As an important problem in radar system, signal detection has been concerned by scholars at home and abroad, mainly because signal detection has been widely used in both military and civil fields, such as radar detection. In order to obtain better anti-jamming and higher resolution, radar systems often use wide-band signals as transmitting signals, but under the traditional Nyquist sampling framework. Large time broadband width is bound to bring a large amount of data acquisition, transmission. Storage and processing problems. The emergence of compression sensing theory is an effective tool to solve this problem. Polynomial phase signal (PPS) is one of the commonly used radar wideband signals. Therefore, this paper will study the detection of PPS, combined with the compression sensing theory, analysis and implementation of PPS signal detection algorithm. Firstly, this paper studies the sparse representation of PPS signal. Then we construct different sparse dictionaries for different PPS signals, and construct waveform delay dictionaries and FRFT orthogonal basis dictionaries for second-order PPS signals, that is, LFM signals. The experiments show that the two dictionaries can represent the LFM signals sparsely, but the FRFT dictionaries can resist white noise more strongly, especially for the third-order PPS signals. The waveform matching dictionary is constructed for it. Secondly, the establishment of compression sensor detection model and the design and implementation of compression detection algorithm based on the detection model are studied. Firstly, a detection method based on Gao Si white noise channel is established. Model. The existing detection methods are based on sparse coefficient location detection algorithm. But its detection effect is not good under the condition of low signal noise. Then the normalized residuals are introduced into the detection algorithm, and according to the different applications of LFM signal and third-order PPS signal in radar signal, the LFM signal is targeted. The normalized residual detection algorithm is introduced to verify the effectiveness of the algorithm. Then multi-pulse detection is introduced into LFM signal detection and a multi-detection algorithm and an accumulation detection algorithm are proposed. The simulation results show that the two multi-pulse detection algorithms improve the detection performance compared with the normalized residual detection algorithm of single pulse, and the cumulative detection algorithm has more advantages in performance. For third-order PPS signals. In this paper, the normalized residual detection algorithm based on multi-component model is studied. According to the characteristic of normalized residual slope, a source number estimation algorithm is proposed. Finally, the validity of this algorithm for estimating the number of information sources is verified.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.51

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