StarBOA：ISAC网络中稀疏雷达微多普勒的实时Mamba状态空间展开
StarBOA: Real-Time Mamba State-Space Unrolling for Sparse Radar Micro-Doppler in ISAC Networks
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中文总结 AI 辅助
StarBOA用因果Mamba状态空间模型替代注意力，在ISAC雷达稀疏数据下实现实时因果重建，SSIM增益随缺失率提升，最高达+0.2472。
中文摘要 AI 辅助
在集成感知与通信（ISAC）中，雷达感知必须在高达90%数据缺失的啁啾子采样条件下工作。一个基于注意力的基线方法，受限于52毫秒的缓冲区，随着稀疏度的增加而趋向于最大均匀熵（$H=2.584$比特），无法捕获长距离步态周期上下文。我们提出StarBOA，它用因果Mamba状态空间模型替代注意力，该模型在逐窗口基础上增量更新，无需重新扫描过去的重建结果。通过维持持久状态，StarBOA在不增加每步计算成本的情况下整合了超过$100\ imes$的时间历史。StarBOA在所有稀疏度水平上均优于基线的已发表结果，SSIM增益从50%缺失数据时的$+0.0379$增加到90%时的$+0.2472$。每个窗口处理时间为1.53毫秒，零前瞻，展示了在极端啁啾子采样下的高效因果重建。
英文摘要
In Integrated Sensing and Communications (ISAC), radar sensing must operate under chirp subsampling with up to 90\% missing data. An attention-based baseline, limited to a 52~ms buffer, collapses toward maximum uniform entropy ($H=2.584$ bits) as sparsity increases, failing to capture long-range gait-cycle context. We propose StarBOA, which replaces attention with a causal Mamba state-space model that updates incrementally on a per-window basis without re-scanning past reconstructions. By maintaining a persistent state, StarBOA integrates over $100\times$ more temporal history at no additional per-step computational cost. StarBOA outperforms the baseline's published results across all sparsity levels, with SSIM gains increasing from $+0.0379$ at 50\% missing data to $+0.2472$ at 90\%. Each window is processed in 1.53~ms with zero lookahead, demonstrating efficient causal reconstruction under extreme chirp subsampling.
