无线电工程

针对宽频带无人机信号监测采样率要求高、响应时间长的问题，提出了一种基于压缩感知的分段式宽带频谱快速采样方法。研究采用分段式超宽带处理架构，将50 MHz～6 GHz的宽频段信号分解为多个500 MHz子带，减轻了每个采样单元的硬件负担，避免了高采样速率和频谱扫描速度的工程瓶颈。通过多通道并行压缩采样与重构，并结合高精度的稀疏重构算法，有效提高了信号重构精度，在低信噪比(Signal to Noise Ratio，SNR)环境下保持了较好的性能。仿真实验表明，该方法能够在低速采样条件下高效捕获无人机通信等低功率瞬时信号，且在重构后保持了均方误差(Mean Squared Error，MSE)低于1×10^-3、重构SNR高于12 dB、相关系数超过0.9的性能，表明分段式压缩感知架构具有良好的工程应用前景。

To address the high sampling-rate requirements and long response times for wideband unmanned aerial vehicle signal monitoring， this work proposes a segmented wideband rapid-sampling method based on compressed sensing. The study adopts a segmented ultra-wideband processing architecture that decomposes the 50 MHz–6 GHz wideband signal into multiple 500 MHz subbands， thereby reducing the hardware burden on each sampling unit and avoiding engineering bottlenecks imposed by high sampling rates and slow spectrum scanning. By employing multi-channel parallel compressed sampling and reconstruction together with high-precision sparse recovery algorithms， the approach substantially improves reconstruction accuracy and maintains robust performance in low Signal to Noise Ratio(SNR)environments. Simulation results show that the proposed method can efficiently capture low-power transient signals such as unmanned aerial vehicle communications under low-rate sampling. After reconstruction， it achieves a Mean Squared Error (MSE)below1×10^-3， a reconstruction SNR exceeding 12 dB， and a correlation coefficient above 0.9， indicating that the segmented compressed-sensing architecture has promising prospects for practical engineering applications.