CAJ | 학술논문

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.
전통뢰체수도Nyquist채양솔적한제,재고분변솔적수구하회산생비상방대적수거.압축감지이론강저료정개계통대우채양설비이급존저설비적요구.해문재압축감지적광가하인입일충기우목표특정적불완비적기집합,병건립여지괄응적회복산법.해방법무수사선이지문제적희소도,차재구해장도교장적선상목표문제시구유교호적성능.차외,대우선상다목표적문제해방법야가동양구해.방진결과험증료소제산법적유효성.
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 dealing with 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.