CAJ | 학술논문

针对有效核函数（active kernel function）未知的联合平移不变子空间（Union of Shift-InvariantSubspaces,USI）,提出了一种压缩采样模型,基于稀疏重构理论,该采样模型能够有效降低信号的采样率。首先建立一个多脉冲雷达回波信号模型,在信号的延时-多普勒平面上对延时轴离散化,将回波信号表示为USI信号;然后在根据构建的压缩采样模型降低信号采样率的同时,利用稀疏贝叶斯学习和ESPRIT算法由信号样本值估计出雷达回波信号的延时、多普勒频移和反射系数等参数;最后仿真验证了研究结论的有效性。
침대유효핵함수（active kernel function）미지적연합평이불변자공간（Union of Shift-InvariantSubspaces,USI）,제출료일충압축채양모형,기우희소중구이론,해채양모형능구유효강저신호적채양솔。수선건립일개다맥충뢰체회파신호모형,재신호적연시-다보륵평면상대연시축리산화,장회파신호표시위USI신호;연후재근거구건적압축채양모형강저신호채양솔적동시,이용희소패협사학습화ESPRIT산법유신호양본치고계출뢰체회파신호적연시、다보륵빈이화반사계수등삼수;최후방진험증료연구결론적유효성。
For the signals in union of shift-invariant subspaces (USI) when the active kernel functions are unknown, a concrete compressed sampling scheme is proposed which can reduce the sampling rate effectively based on the sparse reconstruction. A signal model of multiple-pulse radar echo signal is estabilished firstly, the echo signal corresponds to a signal in union of shift-invariant subspaces by discretizing the delay of delay-doppler space. Furthermore, the parameters of echo signal are estimat- ed from the samples by sparse Bayesian learning and ESPRIT algorithm. Finally, simulation are car- ried out to prove the validity of the research result.