Machine Learning of Behavioural Models for Improved Multi-Sensor Fusion

用于改进多传感器融合的行为模型的机器学习

• 批准号: 2889812
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889812
• 关键词: Machine; Learning; Behavioural; Models; Improved

SSensor fusion is increasingly critical in the defense and security sectors, enhancing decision-making by providing operators with integrated data from diverse sources. This capability is particularly valuable on platforms such as unmanned aerial vehicles (UAVs), where it enables less sophisticated sensors to collectively emulate the functionality of more advanced systems, thereby reducing costs and enhancing resilience. The efficacy of sensor fusion heavily relies on the accuracy of behavioral models used to predict the movements of surveillance targets. Current models, predominantly based on simplistic, nearly constant velocity assumptions, fall short in addressing complex behaviors and deception tactics used by targets.This PhD research aims to address these shortcomings by developing advanced behavioral models through machine learning techniques, utilizing asynchronous, historic multi-sensor data. Despite the potential of machine learning in this context, its application has been limited due to the complexity of dynamically modeling target behavior while managing non-linear measurements and handling detection anomalies. Recent advancements in computational techniques, such as extensions to Particle Markov Chain Monte Carlo (Particle-MCMC) that utilize gradient information and leverage modern deep learning compute resources, offer promising solutions to these challenges.This project will explore and adapt these cutting-edge techniques to improve the predictive accuracy of behavioral models derived from historic surveillance data. The research will assess the utility of these models within the framework of generic multi-sensor fusion and in scenarios specific to Leonardo-a leading entity in defense and security-potentially encompassing the integration of electronic surveillance, radar, and electro-optical/infrared (EO/IR) data. Success in this endeavor could significantly advance multi-sensor data fusion research, bolstering Leonardo's capabilities and positioning the researcher as a future leader in data science within a vital sector committed to national security.

Ssensor Fusion在国防和安全部门中越来越重要，通过向操作员提供来自不同来源的综合数据来增强决策。这种能力在无人机（UAV）等平台上特别有价值，在该平台上，它使得不太复杂的传感器能​​够集体模仿更先进的系统的功能，从而降低了成本并增强了弹性。传感器融合的功效在很大程度上取决于用于预测监视目标运动的行为模型的准确性。当前的模型主要基于简单的，几乎恒定的速度假设，在解决目标所使用的复杂行为和欺骗策略方面缺乏。该博士研究旨在通过使用机器学习技术来通过机器学习技术来解决这些缺点，并利用机器学习技术来解决这些缺点，并采用异型，历史悠久的，历史悠久的，历史悠久的，多发的传感器数据。尽管在这种情况下进行机器学习的潜力，但由于在管理非线性测量和处理检测异常的同时，由于动态建模目标行为的复杂性而受到限制。计算技术的最新进展，例如使用粒子马尔可夫链蒙特卡洛（Monte Carlo）（粒子-MCMC），利用梯度信息并利用现代深度学习计算资源，为这些挑战提供了有希望的解决方案。该项目将探索并适应这些尖锐的技术，以改善从历史悠久的Surveillase Surveillance数据中获得的行为模型的预测准确性。该研究将在通用多传感器融合的框架内评估这些模型的实用性，以及在国防和安全性方面特有的领先实体的方案中，涵盖了电子监视，雷达，雷达和电子光学/红外线（EO/ir）数据的整合。在这项努力中的成功可以大大提高多传感器数据融合研究，增强莱昂纳多的能力，并将研究人员定位为致力于国家安全的重要部门的数据科学领导者的未来领导者。