WiFi Channel State Information (CSI) has shown promise for single-person gait identification, with numerous studies reporting high accuracy. However, multi-person identification remains largely unexplored, with the limited existing work relying on complex, expensive setups requiring modified firmware. A critical question remains unanswered: is poor multi-person performance an algorithmic limitation or a fundamental hardware constraint? We systematically evaluate six diverse signal separation methods (FastICA, SOBI, PCA, NMF, Wavelet, Tensor Decomposition) across seven scenarios with 1-10 people using commodity ESP32 WiFi sensors--a simple, low-cost, off-the-shelf solution. Through novel diagnostic metrics (intra-subject variability, inter-subject distinguishability, performance degradation rate), we reveal that all methods achieve similarly low accuracy (45-56\%, $σ$=3.74\%) with statistically insignificant differences (p $>$ 0.05). Even the best-performing method, NMF, achieves only 56\% accuracy. Our analysis reveals high intra-subject variability, low inter-subject distinguishability, and severe performance degradation as person count increases, indicating that commodity ESP32 sensors cannot provide sufficient signal quality for reliable multi-person separation.

翻译：WiFi信道状态信息（CSI）在单行人步态识别中展现出潜力，已有大量研究报道其高准确率。然而，多行人识别领域仍鲜有探索，现有有限的研究依赖于复杂、昂贵的需修改固件的实验装置。一个关键问题尚未得到解答：较差的多行人识别性能是算法局限还是根本性的硬件约束？我们使用商用ESP32 WiFi传感器——一种简单、低成本、即用型解决方案，在包含1至10人的七种场景下，系统评估了六种不同的信号分离方法（FastICA、SOBI、PCA、NMF、小波变换、张量分解）。通过新颖的诊断指标（受试者内变异性、受试者间可区分性、性能退化率），我们发现所有方法均达到相似的低准确率（45-56\%，$σ$=3.74%），且统计差异不显著（p $>$ 0.05）。即使表现最佳的方法NMF，准确率也仅为56%。我们的分析揭示了高受试者内变异性、低受试者间可区分性以及随人数增加而急剧恶化的性能表现，这表明商用ESP32传感器无法为可靠的多行人信号分离提供足够的信号质量。