基于增量游走策略的多星在轨组阵压缩感知方法

Multi-Satellite Collaborative Compressive Sensing Method Based on Walk-Incremental Strategy

利用低轨卫星的大规模星座优势,建立多星协同感知系统成为当前研究热点。针对传统集中式协作星间传输负载高以及中心节点故障对频谱监测的影响,提出了一种在轨组阵压缩感知方法。所提算法首先通过在轨宽带压缩采样,降低星上宽带信号采集的硬件成本与传输开销;之后根据各卫星节点的低维采样值,基于增量游走策略,顺序激活各卫星节点,以递增的方式进行协作迭代更新。基于低轨星座多重覆盖观测的互补性优势,运用多星在轨协作优化提高低信噪比下的频谱感知性能。仿真结果表明,与传统集中式协作优化算法相比,所提算法在降低计算复杂度的同时提升了频谱感知效能。

Leveraging the large-scale constellations of low earth orbit(LEO)satellites to develop a multi-satellite cooperation system has be⁃come a research focus.This paper proposes an on-orbit compressive sensing method to address the high transmission load of traditional centralized inter-satellite communication and the impact of central node failures on spectrum monitoring.The proposed algorithm first re⁃duces hardware cost and transmission overhead for onboard wideband signal acquisition through on-orbit wideband compressive sensing.Then,using the sub-Nyquist sampling data from each satellite node and following a walk incremental strategy,the satellite nodes are se⁃quentially activated for collaborative iterative updates.By exploiting the complementary advantages of multi-cover observation within the LEO constellation,multi-satellite cooperation on-orbit optimization enhances spectrum sensing performance under low signal-to-noise ra⁃tios.Simulation results demonstrate that,compared to traditional centralized cooperative optimization algorithms,the proposed method re⁃duces computational complexity while improving spectral sensing efficiency.