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
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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. Radio tomographic imaging (RTI) is an emerging device-free localization (DFL) technology enabling the localization of people and other objects without requiring them to carry any electronic device. Instead, the RF attenuation field of the deployment area of a wireless network is estimated using the changes in received signal strength (RSS) measured on links of the network. This paper presents the use of channel diversity to improve the localization accuracy of RTI. Two channel selection methods, based on channel packet reception rates (PRRs) and fade levels, are proposed. Experimental evaluations are performed in two different types of environments, and the results show that channel diversity improves localization accuracy by an order of magnitude. People can be located with average error as low as 0.10 m, the lowest DFL location error reported to date. We find that channel fade level is a more important statistic than PRR for RTI channel selection. Using channel diversity, this paper, for the first time, demonstrates that attenuation-based through-wall RTI is possible. We present an interactive demonstration of histogram distance-based radio tomographic imaging (HD-RTI), a device-free localization (DFL) system that uses measurements of received signal strength (RSS) on static links in a wireless network to estimate the locations of people who do not participate in the system by wearing any radio device in the deployment area. Compared to prior methods of RSS-based DFL, using a histogram difference metric is a very accurate method to quantify the change in RSS on the link compared to historical metrics. The new method is remarkably accurate, and works with lower node densities than prior methods. With the technical advances in ubiquitous comput- ing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoreti- cal base and develop a localization algorithm for building a zero- configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our division building. The empirical results show the proposed system is quite robust and gives accurate localization results (i.e., with the localization error within 3 meters).