Millimeter-wave (mmWave) sensing enables privacy-preserving, always-on edge perception, but its measurements are often sparse, temporally irregular, and corrupted by high-frequency noise. Existing mmWave pipelines predominantly rely on artificial neural networks (ANNs), which achieve robustness through extensive preprocessing or deep architectures, thereby limiting their efficiency on edge devices. In this work, we study spiking neural networks (SNNs) for mmWave sensing from a mechanism-data alignment perspective. By leveraging the low-pass filtering behavior of leaky integrate-and-fire (LIF) dynamics, we analyze how their implicit temporal filtering interacts with the frequency structure of mmWave signals. Our analysis shows that when discriminative information resides in low-to-mid frequencies, LIF dynamics can inherently suppress high-frequency noise, clarifying when and why SNNs outperform ANNs. Based on this insight, we derive a principled criterion for configuring the membrane decay factor by matching the effective bandwidth of LIF dynamics to the data's discriminative spectral content. Experimental results across four widely used mmWave datasets validate the proposed frequency-matching hypothesis, yielding an average test-accuracy improvement of 6.22% and a 3.64$\times$ reduction in theoretical energy consumption relative to ANN baselines, under a unified evaluation protocol.

翻译：毫米波传感技术能够实现隐私保护且始终在线的边缘感知，但其测量结果往往稀疏、时间不规则，并混杂高频噪声。现有毫米波处理流程主要依赖人工神经网络（ANN），通过大量预处理或深层架构实现鲁棒性，这限制了它们在边缘设备上的效率。本研究从机制与数据对齐的视角出发，探索用于毫米波感知的脉冲神经网络（SNN）。通过利用泄漏积分-点火（LIF）动力学的低通滤波特性，我们分析了其隐含的时间滤波机制如何与毫米波信号的频率结构相互作用。分析表明，当区分性信息集中于低频至中频区域时，LIF动力学能够天然抑制高频噪声，从而阐明SNN在何时及为何优于ANN。基于这一发现，我们推导出配置膜衰减因子的基准准则：通过将LIF动力学的有效带宽与数据区分性频谱成分相匹配。在四个广泛使用的毫米波数据集上的实验结果验证了所提出的频率匹配假设，在统一评估协议下，相较于ANN基准，测试准确率平均提升6.22%，理论能耗降低3.64倍。