利用无人海事系统网络开展大范围海洋环境推断

本项目研究无人载具与传感器之间的声学测量数据交换，如何结合声学层析成像、信号处理和机器学习，更准确地估计海洋环境状况。目标是实现海事运行所需的大范围环境感知，并应对传统稀疏海洋观测方法的局限。

About the project Future marine operations will increasingly rely on distributed networks of autonomous platforms. This project will investigate how acoustic measurements exchanged between uncrewed vehicles and deployed sensors can be exploited to estimate ocean environmental conditions over wide areas, combining acoustic tomography, signal processing, machine learning and environmental modelling. Future marine operations are expected to employ large numbers of uncrewed underwater and surface vehicles operating alongside distributed environmental and acoustic sensing systems. The effectiveness of many maritime capabilities, including acoustic communications, navigation, surveillance and sonar systems, depends strongly on environmental conditions. Accurate knowledge of the ocean state is therefore critical for predicting system performance, reducing uncertainty and supporting operational decision-making. However, environmental observations are typically sparse and geographically limited, creating a need for techniques that can infer environmental conditions across much larger areas. While conventional oceanographic sensors provide measurements at discrete locations, acoustic propagation is influenced by environmental parameters including temperature, salinity (leading to sound speed structure) and seabed properties. Acoustic signals transmitted and received across a distributed network therefore contain information about the intervening environment. Extracting and exploiting this information offers the potential to estimate environmental conditions over spatial scales far greater than can be achieved through direct sampling alone. This project will investigate novel approaches for wide-area ocean environmental inference using observations collected by fleets of unmanned vehicles and distributed sensor networks. Drawing on concepts from underwater acoustic tomography, the research will focus primarily on signal processing, estimation and inverse methods, while also exploring the potential application of machine learning and data-driven approaches. Particular emphasis will be placed on developing techniques that are applicable to arbitrary fleets, meaning sensing networks with varying numbers of platforms, differing sensor capabilities, changing geometries and dynamic membership as assets enter, leave or manoeuvre within the network. The project will examine how distributed acoustic observations can be fused with local oceanographic measurements to estimate parameters such as sound speed structure, temperature distribution, water column variability and other environmentally relevant characteristics. The resulting approaches could enable persistent wide-area environmental awareness and support improved prediction and optimisation of sonar, communication and autonomous system performance in future maritime operations.

Entry requirements You must have at least a UK 2:1 honours degree, or its international equivalent , in one of the following: physics mathematics engineering acoustics oceanography computer science a related discipline Desirable skills: quantitative background familiarity with scientific programming (Python, MATLAB or similar tools) familiarity with, or experience in, any combination of signal processing, machine learning, environmental modelling, acoustics, sonar, wave propagation, statistical analysis or numerical modelling experience working with environmental or sensor datasets interest in marine or oceanographic environments

Funding: Competition funded Fees and funding For candidates looking to complete a PhD or EngD full-time we offer an enhanced tax-free stipend of up to £30k per year, along with a substantial budget for research, travel, and centre activities. CISDnS is committed to promoting equality, diversity and inclusivity. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break or are transitioning into a new role. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance.

How to apply Please apply via the online portal and select: Academic year: 2027/28 If you will be full time or part time Faculty: Engineering and Physical Sciences Search for "complex integrated systems" Select either PhD or EngD – if you are unsure, select PhD Add the name of the supervisor in section 2 of the application Applications should include: your CV (resumé) 2 academic references degree transcripts and certificates to date