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，以将静态和移动的海上监视雷达中的低可观察的移动目标与海上混乱背景区分开，这经常看到海杂波回流峰值幅度远大于小目标。通过观察10至100秒的场景，多扫描处理可以检测和跟踪10至20DB“子杂物”的目标，即使在混乱频谱中也是如此。该项目将通过从雷达数据中提取进一步的细节来研究改进。特别是，在检测之前，诸如轨道之类的技术是从杂波频谱中提取子固定移动目标的，这是该项目感兴趣的关键领域。例如，在调整此类算法时可以利用海面分析，以解决该问题，因为它是检测到波场中预期反射率行为的场景中的异常。在项目下，将探索性能改进，算法的处理负载和复杂性水平或相互作用算法集之间的关系。先前的研究已经确定了新型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情况下为目标提取性能提供有用的增强。预计该项目的范围还包括对雷达传感器的特征，包括英国Hensoldt生产的传感器，深入探索Sea-Clutter的特征，感兴趣的目标，以及对计算技术和机器学习的探索，以利用上述计算范式使用传感器数据来补充该项目。与海洋研究与技术社区联络，一项重点的数据收集活动，于2023年秋季开始，自2023年初以来在Cranfield Shrivenham校园的博士学位，以及由英国Hensoldt英国协调的用户社区的背景。