发表机构
School of Electrical and Information Engineering, Zhengzhou University（郑州大学电气与信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单通道同频信号盲分离精度与鲁棒性不足的问题，提出基于序贯检测的迭代分离算法，融合延迟无迹卡尔曼滤波，提升分离与信道估计性能，并显著增强噪声容忍度。
AI 中文摘要
现有的单通道同频信号盲分离（SCSBS）算法难以在分离精度、计算复杂度和鲁棒性之间取得平衡，而当前的信道状态信息（CSI）估计方法精度不足。为解决这些局限，我们提出了一种基于序贯检测（SD）的迭代分离（SDIS）算法。SDIS将延迟无迹卡尔曼滤波器（DUKF）纳入迭代决策反馈框架中，联合增强信号分离与CSI估计。仿真结果表明，SDIS在分离精度、CSI估计精度、计算效率和鲁棒性方面均优于基准算法。值得注意的是，当平均误码率（MBER）降至$10^{-4}$以下时，SDIS比基准算法能容忍至少$0.8$ dB更多的噪声。
英文摘要
Existing single-channel co-frequency signal blind separation (SCSBS) algorithms struggle to balance separation accuracy, computational complexity, and robustness, while current channel state information (CSI) estimation methods lack precision. To address these limitations, we propose a sequential detection (SD)-based iterative separation (SDIS) algorithm. SDIS incorporates a delayed unscented Kalman filter (DUKF) into an iterative decision feedback framework, jointly enhancing signal separation and CSI estimation. Simulation results show that SDIS outperforms benchmarks in separation accuracy, CSI estimation accuracy, computational efficiency, and robustness. Notably, when the mean bit error rate (MBER) drops below $10^{-4}$, SDIS can tolerate at least $0.8$ dB more noise than the benchmarks.
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