This code explores the application of Gaussian
process algorithms and their comparison with standard methods
in real-time target tracking. Utilizing the Stone Soup framework
as an experimental platform, the focus is on the innovative
implementation of Gaussian process and Distributed Gaussian
Process models. Extensive experiments with various kernel con-
figurations demonstrate their critical role in enhancing Gaussian
processes’ predictive accuracy and efficiency, especially in real-
time tracking. The research showcases significant advancements
in tracking capabilities, particularly in wireless sensor networks,
using optimised Gaussian process models. This study advances
the Stone Soup platform’s capabilities and lays the groundwork
for future investigations into adaptive GP applications in tracking
and sensor data analysis
This code explores the application of Gaussian process algorithms and their comparison with standard methods in real-time target tracking. Utilizing the Stone Soup framework as an experimental platform, the focus is on the innovative implementation of Gaussian process and Distributed Gaussian Process models. Extensive experiments with various kernel con- figurations demonstrate their critical role in enhancing Gaussian processes’ predictive accuracy and efficiency, especially in real- time tracking. The research showcases significant advancements in tracking capabilities, particularly in wireless sensor networks, using optimised Gaussian process models. This study advances the Stone Soup platform’s capabilities and lays the groundwork for future investigations into adaptive GP applications in tracking and sensor data analysis
From The University of Sheffield team