This article considers the problem of passive tracking of a noncooperating target indoors. We develop a novel system to localize a moving target using asynchronous self-localizing sniffer nodes, which passively listen to WiFi signals transmitted by the target. The proposed system uses only the time-difference-of-arrival between multipath components (multipath TDoA) at each receiver. This does not require phase synchronization. We develop two novel localization algorithms; one uses batch processing and the other is online. We also design a novel multipath association algorithm. A custom-designed hardware platform is developed to prototype the proposed system. The accuracy of the proposed system is verified experimentally. For signal-to-noise ratio of 10 dB, both proposed algorithms achieve target localization accuracies better than 40 cm with probability 0.95 without needing any knowledge of target or sniffer locations. If the location of one sniffer node is known, the accuracy improves to 15 cm.

This article develops a system for through-wall counting of people walking in a room, based purely on passive reception of the WiFi signals that are generated by devices in that room. We use WiFi compressed beamforming reports, collected using a sniffer node located outside the room. We propose a 2-D discrete Fourier transform (2D DFT) approach for feature extraction. As such, we formulate the counting problem as a multiclass image classification problem. Our proposed system achieves accuracies of 100%, 97.8%, 78.3%, and 93.9% in field trials with zero, one, two, and three people walking inside a room, respectively, even for rooms that were not part of the training set.