PhD Studentship Persistent Coverage Problem for multiple UAV platforms
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PhD Studentship Persistent Coverage Problem for multiple UAV platforms


University of Portsmouth - Portsmouth Business School

PhD Studentship


Project Title: Persistent Coverage Problem for multiple UAV platforms

Application Deadline: Friday 19 April

Supervisors: Dr Jana Ries1, Dr Alessio Ishizaka1, Dr Dylan Jones2

1Operations and Systems Management subject group, Portsmouth Business School

2Department of Mathematics, Faculty of Technology

Project Description

Applications are invited to a three-year PhD Studentship in Portsmouth Business School, starting 1 October 2013.

The persistent surveillance problem consists of covering a particular area of interest (AOI) in order to detect abnormal conditions. In real-life applications, this problem is mainly found in the context of security, for example, in airborne, land-based and sea-based border surveillance scenarios. With differing conditions, vehicle constraints may vary and, therefore, different conditions have to be taken into account including motion and endurance constraints. The problem is mainly solved by using default patterns to ensure a balanced coverage of the AOI. This approach imposes the challenge of adapting to particular scenario-dependent conditions including prioritised regions, obstacles and non-regular shaped AOIs. The supervisory team has worked on the FP7 EU funded project SEABILLA (www.seabilla.eu), where one milestone has been the design of an optimal routing algorithm for Unmanned Aerial Vehicles (UAV) in a persistent surveillance mission. The routing must be efficient (for example, minimise the distance travelled) and effective (for example, maximise the detection rate of abnormal behaviour). This problem has been solved under the assumption that all UAVs have the same capabilities (speed, sensors range, turning angle, etc.). However, in practice UAV fleet capabilities may vary which introduces an additional substantial level of complexity.

This PhD project aims to extend our research in persistent coverage for UAVs with a possible focus on:

• The integration of UAV platforms with different capabilities in the coverage exercise.

• The incorporation of a learning-based concept that is able to adapt the routing in accordance to the new suspicious categorised area by learning from previously detected abnormal behaviour of maritime vehicles.

• The outline of a performance comparison of traditional pattern routing and the new proposed approach.

Funding Status

The studentship will cover tuition fees and an annual grant equivalent to that offered by the ESRC – set at £13,720 per annum for 2013/14 for a maximum of three years. UK/EU residence eligibility conditions apply.

How to apply

Qualifications: Applicants will have a good first degree (minimum 2.1 or equivalent) and ideally a Masters (or equivalent) in a relevant subject area.

Enquiries relating to the topic should be directed to: Dr. Jana Ries (jana.ries@port.ac.uk). This full-time studentship is open to Home/EU students and is located in Portsmouth Business School. Potential applicants are advised to examine our Research Degree Pages at

http://www.port.ac.uk/departments/faculties/portsmouthbusinessschool/researchdegrees/ prior to applying.

Applications should include:

  • a full CV including personal details, qualifications, educational history and, where applicable, any employment or other experience relevant to the application
  • contact details for TWO referees able to comment on your academic performance
  • a statement of 1,000 (words) outlining your proposed project, identifying the objectives of the research and discussing how the work will build on or challenge existing research in the above field.

Interviews will be conducted on Thursday 23 May 2013.

Applications should be sent to: Jana Ries, Postgraduate Centre, University of Portsmouth, Richmond Building, Portland Street, Portsmouth, PO1 3DE (applications can be submitted electronically via: lauren.payne@port.ac.uk and cc to jana.ries@port.ac.uk).




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