The method and technology described in this article are suitable for use cases requiring approximately 2-10 meters of median error.
Keep in mind that accuracy requirements should be tailored to the specific use cases. Some use cases have higher demands for accuracy and a larger budget for indoor positioning infrastructure, while others do not. The solution should strive to strike a balance between accuracy requirements and the budget. In general, investing more money and effort into the infrastructure leads to better accuracy. However, it’s important to note that many use cases can be supported by utilizing existing infrastructure.
The following charts illustrate the expected accuracy based on the number of Wi-Fi/Bluetooth devices and the area’s size in square meters.
Estimated Indoor Accuracy Matrix
The tables show the estimated accuracy you can expect in an indoor environment using AI Indoor (ANN) -based indoor positioning. The accuracy will depend on the Area to be covered and the number of deployed Wi-Fi Access points (APs) or Bluetooth beacons.
The median error accuracy is:
● GREEN from 1 up to 5 meters
● YELLOW 5 up to 10
● Red 10 meters
Table 1 shows how the accuracy improves when using one reference point per 1 square meter in a detailed survey.
Table 2 showing the accuracy if you have performed a detailed survey with one reference point per 4 square meters.
Table 3 showing the accuracy if you have performed a sparse survey with one reference point per 100 square meters.