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.
In @cite_17 , variance-based RTI (VRTI) is presented, which is capable of localizing and tracking people even through walls of a building. A drawback of VRTI is that it is not capable of localizing stationary people since the measurements are based on a windowed variance of RSS. In addition, VRTI is prone to intrinsic motion which is variance of the RSS that is not caused by people. The problem is confronted in @cite_28 , where methods to reduce the noise of VRTI images are presented. Results indicate that the tracking accuracy can be improved significantly by removing the intrinsic noise of the estimated images.
@cite_28 @cite_17
2152178374 2147124620
Human motion in the vicinity of a wireless link causes variations in the link received signal strength (RSS). Device-free localization (DFL) systems, such as variance-based radio tomographic imaging (VRTI) use these RSS variations in a wireless network to detect, locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind, rotating or vibrating machinery, also causes RSS variations which degrade the performance of a DFL system. In this paper, we propose and evaluate a subspace decomposition method subspace variance-based radio tomography (SubVRT) to reduce the impact of the variations caused by intrinsic motion. Experimental results show that the SubVRT algorithm reduces localization root mean squared error (RMSE) by 41 . In addition, the Kalman filter tracking results from SubVRT have 97 of errors less than 1.4 m, a 65 improvement compared to tracking results from VRTI. This paper presents a new method for imaging, localizing, and tracking motion behind walls in real time. The method takes advantage of the motion-induced variance of received signal strength measurements made in a wireless peer-to-peer network. Using a multipath channel model, we show that the signal strength on a wireless link is largely dependent on the power contained in multipath components that travel through space containing moving objects. A statistical model relating variance to spatial locations of movement is presented and used as a framework for the estimation of a motion image. From the motion image, the Kalman filter is applied to recursively track the coordinates of a moving target. Experimental results for a 34-node through-wall imaging and tracking system over a 780 square foot area are presented.