A Multi-Scale Spatial Model for RSS-based Device-Free Localization
This work improves indoor and through-wall localization for applications like surveillance or smart homes, but it is incremental as it builds on existing RSS-based methods.
The paper tackled the problem of device-free localization by showing that spatial impact areas vary per link, and proposed a multi-scale spatial weight model and measurement model, achieving localization accuracy below 0.30 meters in various environments without parameter changes.
RSS-based device-free localization (DFL) monitors changes in the received signal strength (RSS) measured by a network of static wireless nodes to locate people without requiring them to carry or wear any electronic device. Current models assume that the spatial impact area, i.e., the area in which a person affects a link's RSS, has constant size. This paper shows that the spatial impact area varies considerably for each link. Data from extensive experiments are used to derive a multi-scale spatial weight model that is a function of the fade level, i.e., the difference between the predicted and measured RSS, and of the direction of RSS change. In addition, a measurement model is proposed which gives a probability of a person locating inside the derived spatial model for each given RSS measurement. A real-time radio tomographic imaging system is described which uses channel diversity and the presented models. Experiments in an open indoor environment, in a typical one-bedroom apartment and in a through-wall scenario are conducted to determine the accuracy of the system. We demonstrate that the new system is capable of localizing and tracking a person with high accuracy (<0.30 m) in all the environments, without the need to change the model parameters.