摘要
针对传统的脉冲压缩方法存在着副瓣降低与主瓣展宽的矛盾问题,基于压缩感知理论提出了一种实现对LFM信号脉冲压缩的CS脉压算法。首先在分析传统脉冲压缩与压缩感知的关系的基础上,构建了适用于复数域重构的稀疏基,然后提出了采用构建的稀疏基结合平滑0-范数算法实现脉冲压缩的算法,最后证明了所提的算法脉压后信号不仅能重构出回波的幅度,且保留了信号的相位信息,最后对研究的算法从幅度、相位的重构精度以及重构误差等方面进行了仿真,仿真结果表明CS脉压算法能够在不降低距离分辨率的同时达到降低副瓣的目的,同时能保留回波信号的相位历程,具有较高的重构精度。
Most current pulse compression algorithms can＇t avoid the contradiction between the main lobe width and the side lobe level. In this paper, we put forward a modified CS pulse compress（CSPC） algorithm to regain information of amplitude as well as phase of the linear frequency modulation（LFM） signal. In the proposed method, firstly, the relationship between CS and traditional PC is theoretically analyzed. Secondly, sparse base that is adapted to the PC is formulated in complex field. And then, through mathematical deduction, it is proved that smoothed LO norm algorithm combined with the sparse base could reconstruct the amplitude and phase information of the echoes at the same time. Finally, simulation results and theoretical analysis show that the proposed method has many advantages, such as exact reservation of amplitude and phase information of the echoes, high reconstruction accuracy, and better robustness.
出处
《雷达科学与技术》
2013年第3期295-301,共7页
Radar Science and Technology
关键词
脉冲压缩
压缩感知
线性调频
稀疏基
平滑0-范数
pulse compression
compressive sensing
linear frequency modulation（LFM）
sparse base
smoothed LO norm algorithm
