AI 中文总结
本文针对脉冲序列周期估计的时域MLE方法，通过稀疏矩阵乘法核及拆分矩阵投影步骤的优化，降低了运行时间与内存复杂度，实现了实时场景下大型数据集的时域估计。
AI 中文摘要
周期脉冲序列的参数估计是众多自动传感与诊断应用中的关键任务。尽管在低信噪比环境下时域估计能提供更优的精度，但其高计算复杂度常使其无法应用于实时系统。本文研究算法优化，以利用计算架构的最新进展减少运行时间。通过稀疏矩阵乘法核利用信号的固有稀疏性，可大幅降低推理时间。此外，将稠密矩阵投影拆分为互相关与稀疏求和的顺序步骤，从根本上降低了运行时间与内存复杂度。这些优化显著减小了内存占用，使时域估计在实时场景下对大型数据集也具备可行性。
英文摘要
Parameter estimation of periodic pulse trains is a critical task in numerous automated sensing and diagnostic applications. While estimation in the time domain provides superior accuracy in low signal-to-noise ratio environments, its high computational complexity frequently precludes its use in real-time systems. This paper investigates algorithmic optimizations to reduce runtime by leveraging recent advancements in computing architectures. Exploiting the inherent sparsity of the signal via sparse matrix multiplication kernels yields a substantial decrease in inference time. Furthermore, by separating the dense matrix projections into sequential cross-correlation and sparse summation steps, we fundamentally reduce both runtime and memory complexity. These optimizations drastically shrink the memory footprint, making time-domain estimation feasible for large datasets in real-time settings.
Comments6 pages, 6 figures, accepted at 60th Asilomar Conference on Signals, Systems, and Computers (2026)
