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Radio frequency (RF) sensing networks are a class of wireless sensor networks(WSNs) which use RF signals to accomplish tasks such as passive device-freelocalization and tracking. The algorithms used for these tasks usually requireaccess to measurements of baseline received signal strength (RSS) on each link.However, it is often impossible to collect this calibration data (measurementscollected during an offline calibration period when the region of interest isempty of targets). We propose adapting background subtraction methods from thefield of computer vision to estimate baseline RSS values from measurementstaken while the system is online and obstructions may be present. This is doneby forming an analogy between the intensity of a background pixel in an imageand the baseline RSS value of a WSN link and then translating the concepts oftemporal similarity, spatial similarity and spatial ergodicity which underliespecific background subtraction algorithms to WSNs. Using experimental data, weshow that these techniques are capable of estimating baseline RSS values withenough accuracy that RF tomographic tracking can be carried out in a variety ofdifferent environments without the need for a calibration period.

Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks