Description

The Least Squares Localization Algorithm plays a pivotal role in UWB-based positioning systems, enabling precise location estimation by optimizing the fit of measured data to a mathematical model.

Principles of Least Squares Localization in UWB

The Least Squares algorithm is grounded in the concept of minimizing the sum of squared errors between measured UWB range or time-of-flight data and predicted values based on a model. In the UWB context, this algorithm typically involves the following steps:

1. Range measurement collection
2. Defining the objective function and mathematical model
3. Solving the optimization problem
4. Localization results

Advantages of Least Squares Localization in UWB

1. Versatility
2. Scalability
3. Real-time Capability

input: set of ranges between tag and anchors
output: 2D position

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.}