This week, I created the grid-based localization scheme we will use for finding these devices. Right now, it just adds a likelihood based on the time-of-flight of the devices, achieving about 5m of accuracy. We hope with the higher clock frequency of our AX200 board and the directionality we will have from the antennas, we’ll be able to improve even further. This puts us on schedule, and allows me to spend next week working on improving the localization system and handling the ToF measurement processing from the data in a more sophisticated way than we currently do it.
By next week, I hope to have localization accuracy improved to around 1.5m, and through a system that smooths the ToF better than the knowledge-of-the-crowd (averaging) we currently use.
I tested the implementation on an Intel RTT Dataset (Nir Dvorecki, Ofer Bar-Shalom, Leor Banin, Yuval Amizur, June 8, 2020, “Intel Open Wi-Fi RTT Dataset”, IEEE Dataport, doi: https://dx.doi.org/10.21227/h5c2-5439) and attached a picture below: top left is example output, bottom left is our system, the right-hand side is a visualization of what the likelihood function looks like.
