On the Impact of Different Kernels and Training Data on a Gaussian Process Approach for Target Tracking

Aftab, Waqas; Mihaylova, Lyudmila

doi:10.23919/fusion45008.2020.9190413

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…For the GP setting, a zero-mean function is used which means no extra knowledge is utilised for tracking. Besides, the covariance function is selected to be the squared exponential (SE) kernel which is demonstrated to perform well in a wide range of motion models [25]. The SE kernel can be represented as…”

Section: A Simulation Setupmentioning

confidence: 99%

A Learning Distributed Gaussian Process Approach for Target Tracking over Sensor Networks

Liu

Lyu

George

et al. 2022

2022 25th International Conference on Information Fusion (FUSION)

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Tracking manoeuvring targets often relies on complex models with non-stationary parameters. Gaussian process (GP) based model-free methods can achieve accurate performance in a data-driven manner but face scalability challenges. Aiming to address such challenges, this paper proposes a distributed GP-based tracking approach able to learn the kernel hyperparameters in an online manner, to improve the tracking performance and scalability. It caters to the inherent distributed feature of sensor networks and does not need measurements to be transmitted among sensors for target states predictions. Theoretical upper confidence bounds about the tracking error are derived within the regret bound setting. Through this theoretical analysis, the tracking error per time step is upper bounded as a function of predictive variances from local sensors. The theoretical results are supported by simulation based ones over a case study for tracking over wireless sensor networks. With evaluation on challenging target trajectories, a comparison on state-of-theart centralised and distributed GP approaches, numerical results demonstrate that the proposed approach achieves competitively high and robust tracking performance.

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Section: A Simulation Setupmentioning

confidence: 99%

A Learning Distributed Gaussian Process Approach for Target Tracking over Sensor Networks

Liu

Lyu

George

et al. 2022

2022 25th International Conference on Information Fusion (FUSION)

Self Cite

View full text Add to dashboard Cite

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

Liu

Mihaylova

George

et al. 2023

IEEE J. Sel. Top. Signal Process.

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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.

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基于混合驱动高斯过程学习的强机动多目标跟踪方法

Guo,

Teng,

Yin

et al. 2023

Front Inform Technol Electron Eng

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On the Impact of Different Kernels and Training Data on a Gaussian Process Approach for Target Tracking

Abstract: This is a repository copy of On the impact of different kernels and training data on a Gaussian process approach for target tracking.

Cited by 4 publications

References 19 publications

A Learning Distributed Gaussian Process Approach for Target Tracking over Sensor Networks

A Learning Distributed Gaussian Process Approach for Target Tracking over Sensor Networks

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

基于混合驱动高斯过程学习的强机动多目标跟踪方法

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