Radio-Frequency Tomography for Passive Indoor Multitarget Tracking

Radio-frequency (RF) tomography is the method of tracking targets using received signal-strength (RSS) measurements for RF transmissions between multiple sensor nodes. When the targets are near the line-of-sight path between two nodes, they are more likely to cause substantial attenuation or amplification of the RF signal. In this paper, we develop a measurement model for multitarget tracking using RF tomography in indoor environments and apply it successfully for tracking up to three targets. We compare several multitarget tracking algorithms and examine performance in the two scenarios when the number of targets is 1) known and constant, and 2) unknown and time varying. We demonstrate successful tracking for experimental data collected from sensor networks deployed in three different indoor environments posing different tracking challenges. For the fixed number of targets, the best algorithm achieves a root-mean-squared error tracking accuracy of approximately 0.3 m for a single target, 0.7 m for two targets and 0.8 m for three targets. Tracking using our proposed model is more accurate than tracking using previously proposed observation models; more importantly, the model does not require the same degree of training.