基于压缩感知的高分辨率雷达
High-Resolution Radar via Compressed Sensing
AI总结：
本文提出一种将时频平面离散化为N×N网格的压缩感知雷达模型，利用目标稀疏性（K远小于N²）通过非相干脉冲和压缩感知技术重构目标场景，理论给出稀疏度K的上界，数值模拟验证了实际性能更优，相比传统雷达具有更高分辨率潜力。
摘要：
提出了一种风格化的压缩感知雷达，其中时频平面被离散化为N×N网格。假设目标数量K很小（即K远小于N²），那么我们可以发射足够“非相干”的脉冲，并采用压缩感知技术来重构目标场景。给出了稀疏度K的理论上界。数值模拟验证了在实践中可以实现更好的性能。这种新颖的压缩感知方法为超越经典雷达的分辨率提供了巨大潜力。
英文摘要：
A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N by N grid. Assuming the number of targets K is small (i.e., K much less than N^2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of compressed sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel compressed sensing approach offers great potential for better resolution over classical radar.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
