Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking Over Wireless Sensor Networks

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 aspect of the proposed approach consists in the theoretical results are derived that prove 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, whose 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 and the simulation results reveal that it characterizes the presence of the target states in the error bound with 88% and 42% higher probability in X and Y coordinates, respectively, than the confidence intervalbased method.

Sequential GP-UCB Bayesian Optimization for Deep Neural Network Fine-Tuning in Dissolved Oxygen Prediction

“…Gaussian process is a statistical method that updates the intuition on prior distribution (function) by assuming that the function's values at different points are random variables that have a joint Gaussian distribution (Liu et al 2023). In other words, it means that unknown function f is a sample from GP.…”

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi

“…A real-time location is needed using a specific algorithm to estimate the moving target’s exact location. Usually, the radio transmitter (TX) broadcasts a signal within a network, and the target reflects the propagating signal [ 9 , 10 , 11 ]. When the signal is received by the receiver (RX), it can be used to estimate a target’s location [ 12 , 13 , 14 , 15 ].…”

Joint Sensor Scheduling and Target Tracking with Efficient Bayesian Optimisation

“…This paper proposes a BO-assisted approach for active sensing management to search and track a moving target. In contrast to our previous work [16] that focused on distributed tracking, this study emphasises active tracking without any prior position information. The main contribution is two-fold.…”