The anchors are all powered with USB 1A Wall chargers.
The environment has very few objects that can cause physical interference except structural beams at each 20’ and these beams are less than 10 inches wide. This should not be a problem.
I am using the Dev Kit and the data is collected with a Tag connected to a laptop. I was walking on that area to get the data in Tera Term.
The red dots are position that had 0 confidence in data (x, y, z, Confidence), the orange dots had 50 and the green ones had 51 and above. The purple dots are the anchor positions. The units are meters.
I am wondering why the data is not accurate in the red square areas in the pdf?
Are there some cases for which the tag is ranging to 3 or 4 anchors but cannot calculate a position ? If yes I would suspect the anchors coordinates are not accurate enough and not representative of the real network topology
There was not always a direct path as the was physical interference by moment.
But if this was caused because of the physical interference of the structural beams, I would have had small localized areas with lack of data. (The beams are 10 in wide and the map units ar meters)
Here the lack of accurary is spread out, it does not seem to represent straight lines of signt being blocked. With tag position accuracy being 10 cm, we should see the line of sights being blocked in the file…!
I was also facing same problem earlier then I figured out that it was false measurements of anchor placements. On top of that you need to consider the variance matrix for finalizing the position of the anchors based on the number of anchors you are using and the algorithm you are using for position calculation.
No, we can simulate the variance matrix for the fixed anchor positions and see where in the test area we can have the large variance in measurements. In my case if I am keeping mine 4 anchors on the edges of a square it will have large variance in the vicinity, but not when we keep them in different planes due to fact that I am using only 4 anchors as well as the algorithm is more efficient when the vectors are not in a plane.