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Indoor passive localization and tracking: From theory to practice

Target localization and tracking have the inseparable connection. On the one hand, localization is the basis of tracking. On the other hand, tracking can provide localization with prior information to correct the error of localization to some extent. At the same time, making the monitoring area be the grid area can let the localization and tracking system easy to be applied to different monitoring area. This paper mainly studies on the indoor passive localization and tracking in the grid area where `passive' means there is no communication between sensors and the target. The indoor localization and tracking issue is modeled as a vector Kalman filtering problem. Two schemes are developed for the issue: one is based on the optimized weighted centroid algorithm and the other is based on the fingerprint algorithm. Both simulation results and real-world experimental results are used to demonstrate the effectiveness of the developed schemes.