Novel Target Tracking Methods for Combining Passive and Active Sensors (ICASE with Leonardo)

Description

Leonardo manufacture both active sensors (e.g., radars) and passive sensors (e.g., cameras) for use with, for example, air platforms (e.g., F16s and drones). Active sensors can provide accurate range information but emit radiation to do so, reducing the extent to which sensing can be covert. Conversely, passive sensors do not emit radiation in the same way but are not easily configured to provide range information. This studentship relates to fusing the information from such sensors to maximise both the overall system’s performance and the covert nature of the sensing.

Traditionally, the information from such sensors is combined at a high level of abstraction. However, the increasing availability of communications bandwidth and processing power make it possible to consider fusing the data at a lower level of abstraction. This should permit a “cognitive” approach to the fusion of information such that the sensors’ operation are adapted in real-time to explicitly maximise performance and covertness: for example, a radar might be used initially to infer an object’s range but a camera might subsequently be used to maintain a track on the object. Such a cognitive capability is anticipated to improve the fidelity and accuracy of target tracking, in particular.

High performance target tracking algorithms (e.g., particle filters) are based on sequential Bayesian inference. This enables the algorithms to make best use of whatever information is available (e.g., measurements of position and speed but also additional attributes such as colour). Such established algorithms can (at least in theory!) cater with, for example, different individual sensors having different update rates, communication delays causing measurements to arrive out of order, sensor misalignments, systematic biases and using attributes to distinguish multiple objects’ tracks. As well as addressing these challenges, novel extensions to such algorithms will need to be developed to support the development of a cognitive capability. The studentship is therefore likely to draw on recent developments such as: Sequential Monte Carlo samplers; approximations commonly used in the context of Bayesian Networks (e.g., Structured Mean Field, Belief Propagation, Kikuchi approximations).

The PhD aims to develop these methods and to demonstrate performance in the context of a combination of inputs from multiple passive, active sensors and/or multi-function sensors. The objective is to enhance the accuracy and robustness of target tracking by both using advanced methods for processing the data but also by adapting the operation of the individual sensors such that they work synergistically to provide a cognitive capability.

Prof Jason Ralph and Prof Simon Maskell will lead the supervision of the project at the University of Liverpool. Simon and Jason lead a growing vibrant team that currently includes PhD students and post-docs with backgrounds in statistics, maths, computer science, engineering, particle physics and psychology working on applications that span, for example, aerospace, cyber security, insurance, healthcare and robotics. Simon and Jason will be supported by Dr David Grieg (Lead Systems Engineer within the Radar Algorithm and Modelling Group at Leonardo Airborne & Space Systems).

Engagement with and contribution to the state-of-the-art are anticipated and the student will gain international exposure by presenting at top-ranked conferences.

The PhD will be funded for 4 years by an industrial CASE award and includes a top-up of £4500 per year over and above fees and the stipend associated with a standard EPSRC-funded PhD. Extensive collaboration with Leonardo is expected (e.g., including provision of data from their next-generation sensors).

To be eligible, applicants must be a British or other EU national.

To apply please click here.

Availability

Open to EU/UK applicants

Funding information

Funded studentship

The PhD will be funded for 4 years by an industrial CASE award and includes a top-up of £4500 per year over and above fees and the stipend associated with a standard EPSRC-funded PhD. Extensive collaboration with Leonardo is expected (e.g., including provision of data from their next-generation sensors).

Supervisors

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