Description

Particle Filter Localization is a versatile and powerful technique employed in ultra-wideband (UWB) technology for accurate and robust location estimation. It offers a probabilistic framework for tracking and localizing objects in dynamic and often challenging environments. In the context of UWB, particle filter localization has several distinctive characteristics and advantages.

Principles of Particle Filter Localization in UWB

Particle filter localization is based on a probabilistic model of the UWB signal propagation and a dynamic state estimation approach. Here are the fundamental principles of how particle filter localization works in UWB:

1. Particle Representation
2. Measurement Update
3. State Estimation
4. Resampling
5. Prediction
6. Repeat step 2-5
7. Localization Result

Advantages of Particle Filter Localization in UWB

1. Robustness in Dynamic Environments
2. Non-Line-of-Sight (NLOS) Mitigation
3. Multi-Modal Distributions
4. Uncertainty Quantification
5. Versatility

input: a range between tag and a anchor
output: 2D position of the tag

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.}