With the development of wireless technology, Wi-Fi devices are extensively deployed in indoor environments. This fosters the development of Wi-Fi signal-based services and applications, e.g., indoor intrusion detection, human gesture recognition, indoor localization. However, the indoor environments are often complex and variable, Wi-Fi signals from transmitters through multiple paths to reach receivers. There is a large number of Non-Line-Of-Sight (NLOS) paths between the transmitter and the receiver, which causes seriously signal fading, deteriorating the quality of communication links, decreasing the accuracy of recognition application, and increasing the complexity of systems. In this study, an NLOS identification based on the wavelet packet transform (NIWPT) method is proposed. First, NIWPT collects raw channel state information (CSI) signals on the physical layer in current links. Then, NIWPT applies threelayer wavelet packet decomposition on the amplitude of CSI. A set of the wavelet packet coefficient, wavelet packet energy spectrum, information entropy, and logarithmic energy entropy as a feature vector is acquired. After that, the support vector machine is utilized to identify NLOS paths in the current links. Compared with other methods, NIWPT does not need to pre-process the raw CSI signals, it not only maximally reserves influence of the environment on the propagation signal, but also reflects the indoor environment more truly. The experimental results indicate that the recognition accuracy rate of the NIWPT method is 96.23% and 94.75% in the dynamic and static environments, respectively. It proves that the proposed method can effectively identify NLOS paths and has high identification accuracy and universality.
Aiming at the problems of high cost and complex deployment of traditional human behavior recognition method system, a method for obtaining channel state information (CSI) for human behavior recognition using commercial Wi-Fi equipment is proposed. Using the amplitude and phase characteristics in the CSI as the base signal, the power spectrum entropy is used as a new feature to build a fingerprint library. The support vector machine (SVM) based on artificial fish swarm algorithm (AFSA) is used to classify and identify the action. The optimization of the classification is achieved by optimizing the parameter penalty factor and kernel function parameters in the SVM. According to the verification of real environmental data, the average recognition rate reached 94.64%.