Static Object Classification Using WiFi Signals

Object identification and classification play an important role in a variety of real-world applications, such as surveillance, public safety, and emergency response. The traditional RGB image/video-based object classification suffers from privacy preservation issues. While the alternative of adopting X-rays and mmWave based video processing either suffers from harmful radiation or subpar performance for static object classification. In this paper, we propose a WiFi signal-based object classification approach where objects are classified by analyzing the channel state information (CSI) of transmitted signals from a WiFi access point (AP). We devise a data-driven instance-based machine learning (ML) approach to identify objects in a multiclass classification problem. Using publicly available and our own WiFi CSI dataset, we demonstrate why traditional deep convolutional neural network based approaches prove futile towards static object classification due to the relatively smaller amplitude in the CSI stream. Using CSI dataset from our own lab testbed, we demonstrate that the proposed k-nearest neighbor algorithm (kNN) achieves a high classification accuracy of upto 100% under line of sight (LoS) conditions. The results demonstrate that in the absence of Doppler shift caused by moving objects, our proposed methodology overcomes the challenge related to the static object classification.