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Our contributions include:
1. Enable indoor surveillance capability by using the cooperative information from reflected Multi-Path Components (MPC), and data fusion from Kalman filters.
2. Exploit the propagation models and path loss statistics of well-known material penetration to estimate the relative location of a user.
3. The evaluation results can yield high-accuracy tracking performance, up to 15% better than the existing model, even without physical intrusion into the target environment or empirical signal sample collection
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