Model, simulation and implementation of a mechatronical system for use in a mimicked industrial environment using UWB localization. Task dispatch via Arrowhead. Reproducibly built via Nix.
The data we read from the DWM1001C is the measured coordinates (x, y, z) and a quality factor. This quality factor is only described as such in the device's documentation. Formally, the three coordinates can be modelled as discrete-time random processes. Time should be dedicated to apply stocastic signal theory to the data we collect, but for an initial implementation, I believe a weighted moving average with exponential drop-off should suffice for approximate system positioning. Naturally, the quality factor will act as the weight, but we need to find out how to this factor is calculated so that we are not discarding data of interest when we apply an exponential drop-off. The firmware for the DWM1001C can be downloaded from https://www.decawave.com/product/mdek1001-deployment-kit/.
This issue thus requires the following:
[ ] Dig into the firmware and figure out how the quality factor is calculated.
[ ] Implement a weighted moving average with exponential drop-off for (x, y).
This issue depends on #11.
The data we read from the DWM1001C is the measured coordinates
(x, y, z)and a quality factor. This quality factor is only described as such in the device's documentation. Formally, the three coordinates can be modelled as discrete-time random processes. Time should be dedicated to apply stocastic signal theory to the data we collect, but for an initial implementation, I believe a weighted moving average with exponential drop-off should suffice for approximate system positioning. Naturally, the quality factor will act as the weight, but we need to find out how to this factor is calculated so that we are not discarding data of interest when we apply an exponential drop-off. The firmware for the DWM1001C can be downloaded from https://www.decawave.com/product/mdek1001-deployment-kit/.This issue thus requires the following:
(x, y).