Gaussian process

[Lyuchenyi]

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

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 93.60%. Comparing base (ab6af2b) to head (85c49a1).

Additional details and impacted files

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  Coverage   93.60%   93.60%           
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  Files         202      202           
  Lines       12990    12990           
  Branches     2651     2651           
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  Hits        12159    12159           
  Misses        588      588           
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Flag	Coverage Δ
integration	66.23% <ø> (-0.04%)	⬇️
unittests	89.21% <ø> (ø)

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