中文摘要：

本文利用频带宽度先验信息，提出一种面向信息带宽的自适应调制宽带转换器结构．该结构的总采样率为信号信息带宽的四倍，远小于信号的奈奎斯特采样频率，从而更有效利用采样资源，降低采样数据量，提高处理实时性．通过对该结构中随机波形函数周期的选择，可以实现对系统采样率和系统物理实现复杂度的权衡取舍，从而适应不同场合中的应用．本文通过理论分析给出了该结构实现信号精确重构的充分条件．引入多重信号分类算法，分析了该结构适用此算法的充分条件．本文通过仿真实验对上述分析进行了有效性验证．该系统可以应用于隐形装备的吸波材料的前端特性分析、认知无线电的频谱感知．

英文摘要：

Existing spectrum sensing systems are commonly designed based on the famous Nyquist theorem. With the rapid development of radio frequency technology, the corresponding sampling frequency is so high that many problems may be brought about, such as the increasing hardware complexity, large volume of measurements and difficulties to meet the real time requirement etc. To tackle these problems caused by high sampling frequency, a novel scheme, adaptive modulated wideband converter, is proposed. By exploiting the band width of the narrow bands, the total sampling frequency is proved to be as low as four times of the sum of the narrow bands. There is a trade-off between the sampling complexity and the total sampling frequency for different choices of the repeating frequency of the random function. Sufficient conditions are derived to guarantee exact signal recovery from sub-Nyquist measurements. Conditions of full row rank of the equivalent unknown matrix are also explored to guarantee that the multiple signal classification can be adopted to implement the signal reconstruction. The simulations verify the analysis. This novel scheme can be used to implement front-end spectrum analysis for absorbing materials and detect the active channels in cognitive radio.