Xingchi Liu, Lyudmila Mihaylova, Jemin George, Tien Pham
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引用次数: 0
摘要
不确定性量化在无线传感器网络(WSN)上的自主系统、决策和跟踪的开发中发挥着关键作用。然而,在处理传感器收集的不同数据量时,需要提供不确定性置信度,尤其是基于分布式机器学习的跟踪。本文旨在填补这一空白,提出了一种用于点目标跟踪的分布式高斯过程(DGP)方法,并给出了状态估计的置信上限(UCB)。本文的独特贡献包括对提出的方法及其在有杂波测量和无杂波测量情况下的最大跟踪精度提供了理论保证。特别是,所开发的具有不确定性边界的方法是通用的,可以提供可信的解决方案,并提高了可靠性。通过采用泊松测量概率模型,提出了一种新颖的混合贝叶斯滤波方法来增强 DGP 方法。数值结果证明了所提方法的跟踪精度和稳健性。得出的 UCB 是评估 DGP 方法可信度的工具。仿真结果表明,与基于置信区间的方法相比,所提出的 UCB 在 X 坐标和 Y 坐标上成功包含真实目标状态的概率分别高出 88% 和 42%。
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
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.