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Received signal strength based radio tomographic imaging is a popular device-free indoor localization method which reconstructs the spatial loss field of the environment using measurements from a dense wireless network. Existing methods achieve high accuracy localization using a complex system with many sophisticated components. In this work, we propose an alternative and simpler imaging system based on link level occupancy detection. First, we introduce a single-bounce reflection based received signal strength model, which allows relating received signal strength variations to a large region around the link-lines. Then, based on the model, we present methods for all system components including a classifier, a detector, a back-projection based reconstruction algorithm, and a localization method. The introduced system has the following advantages over the other imaging based methods: i) a simple image reconstruction method that is straightforward to implement; ii) significantly lower computational complexity such that no floating point multiplication is required; iii) each link's measured data are compressed to a single bit, providing improved scalability; and iv) physically significant and repeatable parameters. The proposed method is validated using measurement data. Results show that the proposed method achieves the above advantages without loss of accuracy compared to the other available methods. [ABSTRACT FROM PUBLISHER]

The indoor radio propagation channel is typically modeled as a two-state time-variant process, where one of the states represents the channel when the environment is static, whereas the other state characterizes the medium when it is altered by people. In this paper, the aforementioned process is augmented with an additional state. It is shown that the changes in received signal strength are dictated by: 1) electronic noise, when a person is not present in the monitored area; 2) reflection, when a person is moving in the close vicinity of line-of-sight; and 3) shadowing, when a person is obstructing the line-of-sight component of the transmitter–receiver pair. Statistical and spatial models for the three states are derived, and the models are empirically validated. Based on the models, a link line monitoring system is designed, which aims to, first, estimate the temporal state of the channel using a hidden Markov model, and, second, track a person using a particle filter. The results suggest that the presented system outperforms other state-of-the-art systems in terms of localization accuracy while increasing size of the link's sensing region. [ABSTRACT FROM PUBLISHER]