Xingchi Liu, Lyudmila Mihaylova, Jemin George, Tien Pham
{"title":"Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks","authors":"Xingchi Liu, Lyudmila Mihaylova, Jemin George, Tien Pham","doi":"arxiv-2409.07652","DOIUrl":null,"url":null,"abstract":"Uncertainty quantification plays a key role in the development of autonomous\nsystems, decision-making, and tracking over wireless sensor networks (WSNs).\nHowever, there is a need of providing uncertainty confidence bounds, especially\nfor distributed machine learning-based tracking, dealing with different volumes\nof data collected by sensors. This paper aims to fill in this gap and proposes\na distributed Gaussian process (DGP) approach for point target tracking and\nderives upper confidence bounds (UCBs) of the state estimates. A unique\ncontribution of this paper includes the derived theoretical guarantees on the\nproposed approach and its maximum accuracy for tracking with and without\nclutter measurements. Particularly, the developed approaches with uncertainty\nbounds are generic and can provide trustworthy solutions with an increased\nlevel of reliability. A novel hybrid Bayesian filtering method is proposed to\nenhance the DGP approach by adopting a Poisson measurement likelihood model.\nThe proposed approaches are validated over a WSN case study, where sensors have\nlimited sensing ranges. Numerical results demonstrate the tracking accuracy and\nrobustness of the proposed approaches. The derived UCBs constitute a tool for\ntrustworthiness evaluation of DGP approaches. The simulation results reveal\nthat the proposed UCBs successfully encompass the true target states with 88%\nand 42% higher probability in X and Y coordinates, respectively, when compared\nto the confidence interval-based method.","PeriodicalId":501379,"journal":{"name":"arXiv - STAT - Statistics Theory","volume":"396 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Statistics Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Uncertainty quantification plays a key role in the development of autonomous
systems, decision-making, and tracking over wireless sensor networks (WSNs).
However, there is a need of providing uncertainty confidence bounds, especially
for distributed machine learning-based tracking, dealing with different volumes
of data collected by sensors. This paper aims to fill in this gap and proposes
a distributed Gaussian process (DGP) approach for point target tracking and
derives upper confidence bounds (UCBs) of the state estimates. A unique
contribution of this paper includes the derived theoretical guarantees on the
proposed approach and its maximum accuracy for tracking with and without
clutter measurements. Particularly, the developed approaches with uncertainty
bounds are generic and can provide trustworthy solutions with an increased
level of reliability. A novel hybrid Bayesian filtering method is proposed to
enhance the DGP approach by adopting a Poisson measurement likelihood model.
The proposed approaches are validated over a WSN case study, where sensors have
limited sensing ranges. Numerical results demonstrate the tracking accuracy and
robustness of the proposed approaches. The derived UCBs constitute a tool for
trustworthiness evaluation of DGP approaches. The simulation results reveal
that the proposed UCBs successfully encompass the true target states with 88%
and 42% higher probability in X and Y coordinates, respectively, when compared
to the confidence interval-based method.