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 configurations 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 configurations 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.