一种基于CSI的高鲁棒性步态识别方法

针对目前的室内人员步态识别方法存在计算量大、设备成本高、鲁棒性低等问题，提出一种基于信道状态信息的高鲁棒性室内人员步态识别方法WiKown。通过快速傅里叶变换设置能量指示器监测人员行走行为，将采集的CSI步态数据经滤波与降噪处理后以滑动窗口的方式提取特征值，得到人员步态的CSI信息后建立观测序列，最后通过高斯分布叠加拟合后引入隐马尔科夫模型计算观测序列概率，生成步态参数模型。在走廊、实验室和大厅真实多人环境中，WiKown方法对单人步态的平均识别率达到92.71%。实验结果表明，与决策树、动态时间规整和长短时记忆网络方法相比较，该方法能有效地识别出人员的步态信息，提升了识别精度和鲁棒性。

A highly robust gait recognition method based on CSI

Aiming at the problems of high computational cost, high equipment cost, and low robustness of existing indoor personnel gait recognition methods, this paper proposes a highly robust indoor personnel gait recognition method based on Channel State Information (CSI), called WiKown. This method uses the fast Fourier transform to set an energy indicator to monitor the occurrence of walking behavior. The collected CSI gait data are filtered and de-noised. Then, the characteristic values are extracted in the form of the sliding window and the observation sequence is established. Finally, Gaussian Mixture Model (GMM) is used to superimpose and fit the sequence, and then Hidden Markov Model (HMM) is used to calculate the probability of observation sequence to generate the gait parameter model. The method can effectively identify the gait information. In the real multi-person environment of corridors, laboratories, and halls, the average recognition rate of the WiKown method for a single persons gait can reach 92.71%. The experimental results show that, compared with the decision tree, dynamic time structuring, and long-duration memory network methods, this method can effectively identify gait information and improve the recognition accuracy and robustness.