Job Description

Automatic target classification performance of modern radar and sonar systems in highly cluttered environments is much poorer than that required by many emerging defence and civil applications.
Echolocating bats have evolved an excellent ability to discriminate targets even in highly cluttered environments. They have had some 52 million years of evolution to optimise their echolocation system and they show outstanding discrimination performance even though their brain, which is as small as a nut, can only cater for low computational loads. The way bats process echoes from targets and the surrounding environment is significantly different from that of modern radar and sonar systems. Exploiting novel signal design and processing techniques that take inspiration from the adaptive echolocation behaviour and the auditory system of bats may be of enormous benefit to the task of radar and sonar target classification and can contribute to the future development of high level classification performance, low-computational and low-cost radar and sonar systems. The aims of the PhD programme are:
• to develop non-conventional signal processing algorithms for radar and sonar automatic target classification (ATC) that take inspiration from the most advanced auditory computational models of echolocating bats and that are based on adaptive waveform diversity and multi perspective target information
• to develop a radar prototype and run a set of experimental trials
• to assess and compare the algorithm performance in highly cluttered environments with that of conventional methods.
The successful candidate will take inspiration from the bat auditory system and using observations from past behavioural experiments will develop a biologically inspired non-conventional adaptive signal processing algorithm for target classification. The algorithm will jointly adapt waveform design and target look aspect angle so to maximise discrimination performance given a set of targets. The algorithm will have the flexibility to process information gathered with multiple waveform designs, including real bat echolocation calls. The key goal is to use adaptation in both waveform and in orientation together with a new processing scheme to improve classification in heavy clutter.
The successful candidate will significantly benefit from multidisciplinary cooperation. He/she will benefit from working and bridging between disciplines and from a daily contact with both experts in biological sciences and engineering.

Stipend: up to 15,000.00 GBP per annum (tax free)
Nationality Requirement: Applicants must be nationals of an EU member state
Start Date: As soon as possible
Location: Cranfield Defence and Security, Defence Academy of the UK, Shrivenham, UK
To apply email your CV together with your university transcripts to Dr Alessio Balleri (a.balleri@cranfield.ac.uk)

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