计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 343-348.doi: 10.11896/jsjkx.200700006
• 信息安全 • 上一篇
王颖颖, 常俊, 武浩, 周详, 彭予
WANG Ying-ying, CHANG Jun, WU Hao, ZHOU Xiang, PENG Yu
摘要： 目前,Wi-Fi已被广泛应用于公共和私人领域,基于无线技术的无设备人体入侵检测在实现资产安全、应急响应和个性化服务等室内服务中有着广泛的应用前景。针对现有方法存在误报和漏报严重、海量信息难以分析、部署麻烦等问题,文中提出了一种基于Wi-Fi 信号的入侵检测方法。首先利用Wi-Fi设备上细粒度的信道状态信息(Channel State Information,CSI)捕捉由人体移动引起的微小变化;其次利用多重信号分类算法(Multiple Signal Classification,MUSIC)采样协方差矩阵特征分解得到的噪声子空间以估计目标到达角度(Angle of Arrival,AOA);最后通过计算人体移动导致的不同路径相位差变化来判断是否有人入侵。与传统方法的区别在于,所提方法将谱峰搜索和相位差相结合,二者优势互补,克服了环境和噪声干扰,解决了多径效应对结果的影响。文中选取两种典型的室内环境——会议室和暗室来测试该方法的有效性。实验结果显示,所提方法在两种室内环境中的平均假阴性(False Negative,FN)和假阳性(False Positive,FP)分别为1.83%和1.4%。此外,文中还评估了所提方法在不同运动模式下的检测性能,平均假阴性和假阳性分别为2.26%和1.46%。与其他方法的对比结果验证了该方法的有效性和稳定性。该方法具有很强的鲁棒性和实用价值,为今后入侵检测技术的发展提供了参考方案。
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