Filtering Noisy 802.11 Time-of-Flight Ranging Measurements From Commoditized WiFi Radios
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Time-of-flight (ToF) echo techniques have been recently suggested for ranging mobile devices over WiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes our method particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments and, through extensive evaluations across four different testbeds covering areas up to 1000 m2, the filter is able to achieve a median 2-D positioning error between 2 and 3.4 m.