Radar systems sense the surrounding environment by transmitting a short electro-magnetic waveform and by listening, with a receiver, to echoes produced by reflecting objects. The presence of a target is declared when the amplitude of a radar return at the receiver is higher than a pre-defined threshold. Radar clutter is defined as the echo returned to the radar by environmental scatterers that surround a target of interest.

 

As such, at the receiver, clutter returns compete with target echoes and the radar must be capable of discriminating between the clutter and the target to provide a reliable and useful detection. Examples of radar clutter are volume clutter, ground clutter and sea clutter, that is reflections from objects in the air (rain, snow, wind-blown sand), on the ground (such as hummocks, hillsides, grass, trees and leaves) and from the sea surface (ripples and waves).

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One of the main challenges of providing reliable detection performance in clutter is the ability of the radar to control the so-called probability of false alarm. There are, in fact, situations when although the target is not present, the clutter return is so high that the amplitude of the clutter echo exceeds the detection threshold; in this case, the radar declares a target by mistake producing a false alarm.

 

This issue is particularly challenging for marine navigation and surveillance radars because the strengths of the radar reflections from the sea-surface vary dramatically in time and space, due to the structure and motion of waves, as well as a function of weather conditions and the operational geometry. These reflections can be stronger than reflections from the targets of interest, requiring a more complex differentiation between the two.

 

To achieve reliable detection in sea-clutter and to control the probability of false alarm, a radar needs to account for the inherent character and varying statistics of clutter returns and employ Constant False Alarm Rate (CFAR) detection techniques that automatically vary detection thresholds to keep the probability of making an erroneous detection constant. These techniques and solutions are inherently unique to the characteristics and operational parameters of each radar.

 

In this project, we propose to develop and document an intimate knowledge of the application, the current products and solutions, the subject matter area and the state of the art, and then to make use of large volumes of data collected with the marine radars produced by Hensoldt UK to explore, identify and demonstrate novel methods for advancing the state of the art.

 

This will be achieved by starting with familiarisation through bookwork, training and hands-on experience supported by Hensoldt UK, proceed through a wide-range literature study on theory, established method, performance metrics and modelling techniques, and then lead on to proposing, agreeing and demonstrating novel approaches to extract targets from clutter, which offer a performance improvement.

At a glance

  • Application deadline31 Aug 2022
  • Award type(s)PhD
  • Start date03 Oct 2022
  • Duration of award3 years
  • EligibilityUK, EU, Rest of World
  • Reference numberCDS036

Supervisor

Main supervisor - Dr Alessio Balleri


Entry requirements

Applicants should have a Masters degree in an appropriate subject (Computer, Electrical or Electronic Engineering, Mathematics or Physics) and a 1st class or 2.1 UK Honours degree (or equivalent from an overseas university)

Funding

Sponsored by Hensoldt UK and Cranfield University under the Cranfield Industrial Partnership PhD Scholarships Scheme. This studentship will provide a salary of £16,000 per annum (tax free) and will cover all registration fees for three years.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

If you are eligible to apply for this research studentship, please complete the online application form.   Please quote the following title and reference number: PhD in Statistical Analysis of Experimental Sea Clutter Data and Development of Clutter Rejection and Target extraction algorithms - CDS036. In addition to the application form, please attach a covering or motivational letter.

For further information contact us today:

CDS Admissions office
E: cdsadmissionsoffice@cranfield.ac.uk