无线传感器网络下分布式点目标跟踪中的高斯过程置信上界
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
AI总结：
针对无线传感器网络分布式跟踪的不确定性量化需求，提出带置信上界的分布式高斯过程点目标跟踪方法及混合贝叶斯滤波增强方案，经仿真验证精度、鲁棒性更优，置信界可信度显著提升。
摘要：
不确定性量化在无线传感器网络（WSNs）上的自主系统开发、决策制定和跟踪任务中发挥着关键作用。然而，目前亟需为基于分布式机器学习的跟踪方法提供不确定性置信界，以应对各传感器收集到的不同体量数据的问题。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，并推导了状态估计的置信上界（UCBs）。本文的一项独特贡献在于，针对所提方法以及其在存在和不存在杂波测量两种场景下的跟踪最大精度，推导了理论保证。特别地，所开发的带不确定性界的方法具有通用性，能够提供可靠性更高的可信解决方案。本文还提出了一种新颖的混合贝叶斯滤波方法，通过采用泊松测量似然模型来增强DGP方法的性能。研究在传感器感知范围有限的WSN案例场景中验证了所提方法的有效性。数值结果证明了所提方法的跟踪精度和鲁棒性。推导得到的UCBs可作为评估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UCBs在X坐标和Y坐标上包含真实目标状态的概率分别高出88%和42%。
英文摘要：
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.
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