Developing Novel Bayesian Track Before Detect Approaches for Maritime Big Data Challenges

在检测方法之前开发新颖的贝叶斯轨迹应对海事大数据挑战

• 批准号: 2889729
• 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-2889729
• 关键词: Developing; Novel; Bayesian; Track; Before

This project aims to research novel signal processing methods to separate low observable moving targets from a sea clutter background in static and moving maritime surveillance radar, which frequently see sea clutter return peak amplitudes that are far greater than those from small targets. By observing scenes for 10s to 100s of seconds, multi-scan processing allows detection and tracking of targets 10 to 20dB 'sub-clutter', even within the clutter spectrum. This project will study improvements achievable by extracting further detail from the radar data. In particular, techniques such as track before detect to extract sub-clutter moving targets from within the clutter spectrum is a key area of interest for this project. Sea surface analysis could be exploited in the tuning of such algorithms, for example, to approach the problem as one of detecting anomalies in a scene that deviate from expected reflectivity behaviour in a wave field. The relationship between performance improvements, the processing load and level of complexity of algorithms or interacting algorithm set, is to be explored under the project. Previous research has identified novel effective Monte Carlo algorithms which utilise gradient-based MCMC proposals which can provide useful enhancements to target extraction performance in scenarios that will be determined early in the project. The scope of the project is expected to also include characterisation of radar sensors including those produced by Hensoldt UK, deep exploration of the characteristics of sea-clutter, and of targets of interest, and exploration of computational techniques and machine learning to leverage the above mentioned computational paradigm with sensor data.The project may be complemented by research outcomes from associated academic partners, part-funded by Hensoldt, and Hensoldt staff, as well as liaison with Oceanographic Research and technology community, a focussed data collection campaign commencing autumn 2023, a PhD at the Cranfield Shrivenham campus since early 2023 and context from a user community coordinated by Hensoldt UK.

该项目旨在研究新颖的信号处理方法，以将低可观测移动目标与静态和移动海上监视雷达的海杂波背景分开，这些雷达经常看到远大于小目标的海杂波返回峰值幅度。通过观察场景 10 到 100 秒，多重扫描处理可以检测和跟踪 10 到 20dB 的“亚杂波”目标，即使在杂波频谱内也是如此。该项目将研究通过从雷达数据中提取更多细节可实现的改进。特别是，诸如检测前跟踪等从杂波频谱中提取子杂波移动目标的技术是该项目感兴趣的关键领域。海面分析可用于调整此类算法，例如，将问题视为检测场景中偏离波场中预期反射率行为的异常情况之一。该项目将探索性能改进、处理负载和算法或交互算法集的复杂程度之间的关系。先前的研究已经确定了新颖有效的蒙特卡洛算法，该算法利用基于梯度的 MCMC 建议，可以在项目早期确定的场景中为目标提取性能提供有用的增强。该项目的范围预计还包括雷达传感器（包括英国亨索尔特生产的雷达传感器）的表征、对海杂波特征和感兴趣目标的深入探索，以及对利用上述技术的计算技术和机器学习的探索该项目可能会得到相关学术合作伙伴的研究成果的补充，部分由亨索尔特和亨索尔特工作人员资助，以及与海洋学研究和技术社区的联络，这是一项将于秋季开始的集中数据收集活动2023 年，自 2023 年初以来在克兰菲尔德什里文纳姆校区获得博士学位，背景来自 Hensoldt UK 协调的用户社区。