Summary of Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking Over Wireless Sensor Networks, by Xingchi Liu et al.
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
by Xingchi Liu, Lyudmila Mihaylova, Jemin George, Tien Pham
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a critical gap in uncertainty quantification for distributed machine learning-based tracking over wireless sensor networks (WSNs). Specifically, it proposes a distributed Gaussian process (DGP) approach for point target tracking with theoretical guarantees on its accuracy and derives upper confidence bounds (UCBs) of the state estimates. The DGP approach is enhanced by a novel hybrid Bayesian filtering method using a Poisson measurement likelihood model. Numerical results validate the tracking accuracy and robustness, demonstrating the effectiveness of the proposed UCBs in evaluating trustworthiness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create trustworthy solutions for tracking targets with uncertainty. It develops a way to track points using wireless sensors, which is important for decision-making and autonomous systems. The method uses Gaussian processes, which are good at handling uncertain data. The team also came up with a new way to combine this method with Bayesian filtering, making it more reliable. They tested their approach on a real-world example and showed that it works well. |
Keywords
» Artificial intelligence » Likelihood » Machine learning » Tracking
