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.
The use of channel diversity to enhance DFL accuracy is presented in @cite_9 . The work ranks the different frequency channels used for communication based on two parameters: packet delivery ratio and channel fade level. The results demonstrate that fade level is a more important factor than communication performance when accurate DFL is required. Through channel diversity, the localization accuracy is shown to improve by an order of magnitude compared to a system communicating on a single channel. The multi-channel system in @cite_9 is used in a long-term residential monitoring application in @cite_26 . The work demonstrates the applicability of RSS-based DFL in real domestic environments and the ability of this technology to achieve high localization accuracy over extended periods of time.
@cite_9 @cite_26
2006327170 2031083855 2118097499 2008025584
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 consider the use of spatial diversity to improve the performance of analog joint source-channel coding in wireless fading channels. The communication system analyzed in this paper consists of discrete-time all-analog-processing joint source-channel coding where Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are employed. By assuming a fast-fading Rayleigh channel, we show that MMSE performs much better than ML at high Channel Signal-to-Noise Ratios (CSNR) in single-antenna wireless systems. However, such performance gap can be significantly reduced by using multiple receive antennas, thus making low complexity ML decoding very attractive in the case of receive diversity. Moreover, we show that the analog scheme can be considerably robust to imperfect channel estimation. In addition, as an alternative to multiple antennas, we also consider spatial diversity through cooperative communications, and show that the application of the Amplify-and-Forward (AF) protocol with single antenna nodes leads to similar results than when two antennas are available at the receiver and Maximal Ratio Combining (MRC) is applied. Finally, we show that the MMSE implementation of the analog scheme performs very close to the unconstrained capacity of digital schemes using scalar quantization, while its complexity is much lower than that of capacity-approaching digital systems. Device-free localization (DFL) is the estimation of the position of a person or object that does not carry any electronic device or tag. Existing model-based methods for DFL from RSS measurements are unable to locate stationary people in heavily obstructed environments. This paper introduces measurement-based statistical models that can be used to estimate the locations of both moving and stationary people using received signal strength (RSS) measurements in wireless networks. A key observation is that the statistics of RSS during human motion are strongly dependent on the RSS "fade level” during no motion. We define fade level and demonstrate, using extensive experimental data, that changes in signal strength measurements due to human motion can be modeled by the skew-Laplace distribution, with parameters dependent on the position of person and the fade level. Using the fade-level skew-Laplace model, we apply a particle filter to experimentally estimate the location of moving and stationary people in very different environments without changing the model parameters. We also show the ability to track more than one person with the model. Device-free localization (DFL) enables localizing people by monitoring the changes in the radio frequency (RF) attenuation field of an area where a wireless network is deployed. Notably, this technology does not require people to participate in the localization effort by carrying any electronic device. This paper presents a DFL system for long-term residential monitoring. Due to the daily activities carried out by the people being monitored, the radio signals' propagation patterns change continuously. This would make a system relying only on an initial calibration of the radio environment highly inaccurate in the long run. In this paper, we use an on-line recalibration method that allows the system to adapt to the changes in the radio environment, and then provide accurate position estimates in the long run. A finite-state machine (FSM) defines when the person is located at specific areas-of-interest (AoI) inside the house (e.g. kitchen, bathroom, bed, etc.). Moreover, each time a state transition is triggered, the system tweets the new AoI in a Twitter account. The FSM allows extracting higher level information about the daily routine of the person being monitored, enabling interested parties (e.g. caretakers, relatives) to check that everything is proceeding normally in his life. In the long-term experiment carried out in a real domestic environment, the system was able to accurately and reliably localize the person during his daily activities.