Description

In certain scenarios, there might be a lack of adequate anchor-to-anchor ranges to accurately determine the positions of all anchors. This limitation often arises from an insufficient number of Line-of-Sight (LoS) connections with specific anchors or the poor Dilution of Precision (DOP) associated with the chosen network topology. To address this challenge, the tag-assisted self-calibration method offers a practical solution. It employs a mobile tag to collect supplementary data, enabling the creation of LoS links with anchors that were previously inaccurately positioned. This, in turn, enhances the overall precision of anchor positioning. This algorithm is typically run after Multi-hop anchor optimizer.

Principles of Multi-hop anchor optimizer

The tag-assisted self-calibration algorithm is based on the assumption that more new data will always be benificial to the localization of the anchors. In a general sense, it comprises the subsequent procedural stages:

1. Acquire range between a anchor and a tag
2. Use EKF to update position of the tag and the anchor
3. Repeat step 1-2

Advantages of tag-assisted self-calibration

1. Reduced installation time
2. Robust results even Non-Line-of-Sight (NLoS) conditions
3. Scability

input: Set of ranges between anchors and the tag, position of the anchors
output: Set of 2D anchor positions 

Supported languages

1. Python

Supported hardware

This algorithm is meant to be run in the cloud* or on edge devices, thus supporting any hardware capable of determining the ranges between anchors.”

^{*“In the cloud” suggests that it is not designed for deployment on embedded hardware.}