摘要
传统雷达受到Nyquist采样率的限制,在高分辨率的需求下会产生非常庞大的数据。压缩感知理论降低了整个系统对于采样设备以及存储设备的要求。该文在压缩感知的框架下引入一种基于目标特征的不完备的基集合,并建立与之适应的恢复算法。该方法无需事先已知问题的稀疏度,且在求解长度较长的线状目标问题时具有较好的性能。此外,对于线状多目标的问题该方法也可同样求解。仿真结果验证了所提算法的有效性。
In conventional radar system, the resolution is constrained by Nyquist sampling rate. A large amount of data is created under the high-resolution requirement. Compressive Sensing （CS） relieves the demand of A/D converter and the capacity of memories. Under the framework of CS, a set of bases, which is incomplete but is based on the targets＇ features, is given out in this paper. A method is proposed for reconstruction that is compatible with the bases. The sparseness of the issue is not necessary for the proposed approach. And the method has very good performance on dealingwith linear targets, especially when the lengths of the targets are very long. Furthermore, it can also resolve the multi-target issue. The simulation results verify the efficiency of the proposed algorithm.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第3期568-574,共7页
Journal of Electronics & Information Technology
基金
国家973计划项目(2010CB731904)资助课题
关键词
雷达
压缩感知
线状目标
目标特征
迭代恢复算法
多目标
Radar
Compressive Sensing （CS）
Linear target
Targets＇ feature
Iterated reconstruction method
Multi-target
