Low cost acoustic camera designs and algorithms for tracking swarms of drones / UAVs (Ref: AACME-24-033)

Tracking and detection methods for UAVs and drones typically involve human in the loop, with technology including long range radar, short range radar, cameras, infra-red camera, RF communication technology and acoustics.

At shorter range, cameras and acoustic microphones can detect the drones and provide tracking data to a wide distributed sensor network, which includes management and path prediction. This allows time for a person to intercept and address the drone behaviour. Cameras need to know which way to point and in cluttered urban environments suffer performance degradation. In order to know roughly where to point the lens and orient the drone, an acoustic camera can be used to identify where the motor noise and blade noise is coming from. Acoustic arrays of microphones can be designed to find the direction of sound, and there are examples of industrial applications using these.

In this PhD project, the ambition is to study the performance of acoustic camera designs for tracking narrow band noise sources, using arrays of cheaper microphones with lower signal to noise ratios than expensive options. The intention is to simulate a wide sensor network of tracking devices. Beamforming algorithms will be optimised for single drones, multi-drones and swarm options, looking for differences in the mass that could indicate variants. 

The department has a large UAV laboratory, with recent experience of target tracking algorithm development in this field. We wish to further this research by looking specifically at the sensors and sensor management in terms of technical capability, signal to noise problems and blocking / reflections or even contamination of the signal so that these can be included in the simulation environment.

It is anticipated that the project will involve both simulation development, largely in Matlab / Simulink and experimental measurements of real UAV drones with microphone arrays, looking into directional measurements. Hence an interest in UAV movements would be a significant advantage.

The student will be expected to set up a real-time Simulink model of the drone noise with sensors distributed over a 2D area. Beamforming and focusing methods will be used on simulated microphones to develop a minimum number and distribution or whether distant microphones can be compared using a GPS timebase.

Research questions to answer include the beamforming for low quality signals, the implications of this on the wider statistical prediction methods, the ability to categorise the drone by type using either the spectral characteristics or machine learning and how this provides a statistical model of the sensor. 

Loughborough has access to a full size anechoic chamber for experimental measurements, and a full series of data acquisition equipment. It also has a significant capability in UAV measurements with staff from a range of signal processing backgrounds. 

In addition to the sound acquisition, machine learning may be critical to identify drone types from a training database but we might also look at radio frequency signals or radiation of frequency data including electromagnetic signatures and communication protocols as additional sensors using inexpensive software defined radio platforms.

You will be working with Dr Dan O’Boy and Dr Matthew Coombes from the Department of Aeronautical and Automotive Engineering who have a proven track record working with industrial manufacturers and regulators. You will have access to noise experts, control, experimental and machine learning specialists as needed.

94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021

Supervisors

• Primary supervisor: Dan O’Boy
• Secondary supervisor: Matthew Coombes

Entry requirements

Students should have, or expect to achieve a 2:1 undergraduate degree in a relevant subject.

An interest in either acoustics, vibration, UAV, flight control and tracking, radar, beamforming or signal processing would be an advantage.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website.

Fees and funding

The studentship is for 3 years and provides a tax-free stipend of £19,237 per annum for the duration of the studentship, plus university tuition fees.

How to apply

All applications should be made online. Under programme name, select AACME / AAE Department of Automotive and Aeronautical Engineering. Please quote the advertised reference number: * AACME-24-033 * in your application.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.

The following selection criteria will be used by academic schools to help them make a decision on your application: CV and application cover letter, background experience, a two page vision for the project.

Apply now