Dr Francesco Fioranelli
- Lecturer in Glasgow College UESTC (Systems Power and Energy)
telephone: 0141 330 4301
The multiple aspects of developing, testing and using bistatic and multistatic radar systems.
- Human micro-Doppler signatures for security and healthcare applications (gait recognition, activity identification, gesture recognition)
- UAVs and drones detection and classification
- Machine learning algorithms for radar target classification
- Through-wall radar imaging
- Wind farm clutter characterization and mitigation
- Maritime targets and sea clutter characterization
I graduated in Telecomm Engineering (summa cum laude) at the Università Politecnica delle Marche, Ancona, Italy for my Bachelor (2007) and Master (2010). I received my PhD on through-wall radar imaging at Durham Unviersity (UK) in January 2014, and worked as a Research Associate on multistatic radar with Prof Hugh Griffiths at University College London between February 2014 and March 2016.
I then joined the University of Glasgow in April 2016 as a Lecturer in the Glasgow College UESTC, between the University of Glasgow and the University of Electronic Science and Technology of China (UESTC) in Chengdu, for which I am the Exam and Assessment Coordinator, and course coordinator for teaching Circuits Analysis and Design.
I am a member of the IEEE and IET, Chartered Engineer (CEng), and a reviewer for several academic journals including IET Radar, Sonar & Navigation, IEEE Transactions on Aerospace and Electronic Systems and IEEE Sensors.
For prospective students
I am always keen to hear from talented students at both Master and PhD level who have an interest in my research areas (see above) for potential final year projects and PhD thesis work. A background in radar or communication systems, and signal processing is beneficial. Information on current PhD project within the division and the group are available here and information on the application procedure are here.
Fioranelli, F., Ritchie, M. and Griffiths, H. (2016) Performance analysis of centroid and SVD features for personnel recognition using multistatic micro-Doppler. IEEE Geoscience and Remote Sensing Letters, 13(5), pp. 725-729. (doi:10.1109/LGRS.2016.2539386)
Fioranelli, F., Ritchie, M. and Griffiths, H. (2015) Aspect angle dependence and multistatic data fusion for micro-Doppler classification of armed/unarmed personnel. IET Radar, Sonar and Navigation, 9(9), pp. 1231-1239. (doi:10.1049/iet-rsn.2015.0058)
Fioranelli, F., Ritchie, M. and Griffiths, H. (2015) Classification of unarmed/armed personnel using the NetRAD multistatic radar for micro-Doppler and singular value decomposition features. IEEE Geoscience and Remote Sensing Letters, 12(9), pp. 1933-1937. (doi:10.1109/LGRS.2015.2439393)
Fioranelli, F., Ritchie, M., Borrion, H. and Griffiths, H. (2015) Classification of loaded/unloaded micro-drones using multistatic radar. Electronics Letters, 51(22), pp. 1813-1815. (doi:10.1049/el.2015.3038)
Fioranelli, F., Salous, S. and Raimundo, X. (2014) Frequency-modulated interrupted continuous wave as wall removal technique in through-the-wall imaging. IEEE Transactions on Geoscience and Remote Sensing, 52(10), pp. 6272-6283. (doi:10.1109/TGRS.2013.2295835)
- October 2017 - March 2021 - £40,000 - Industrial PhD studentship from Leonardo UK on innovative detection and classification of small UAVs and drones
- July 2017 - July 2018 - £37,000 - SUPERGEN Flexible Funding 2, collaborative project with UCL and University of Manchester for a total of £125,000 - Experimental characterisation and modelling of multistatic multiband radar signatures of large offshore wind turbines
- June 2017 - £3,000 - Support from the International Partnership Development Funding to develop the collaboration between the School of Engineering at UoG and the University of Cape Town, South Africa
- July 2016 - £3,000 - Support from the International Partnership Development Funding to initiate a collaboration between the School of Engineering at UoG and the University of Cape Town, South Africa
- Ali Rizwan - 2nd supervisor with Prof Imran as 1st supervisor
- Haobo Li - Wearable devices and radar for human activities monitoring and classification - Self-funded - Co-supervised with Dr Heidari
- Aman Shrestha - Radar micro-Doppler for healthcare applications - Supported by DTA, School of Engineering - Co-supervised with Dr Le Kernec
Students' projects past and present
- Vlad Coman - Radar absorbing metamaterials: active vs passive - MEng placement at ONERA, France - 2017
- Aleksandar Angelov - Machine learning for automotive radar and sensing - Co-supervised with Prof Roderick Murray-Smith - MEng placement at NXP - 2017
- Charlie Owens - Processor Architecture Analysis of Task-Based Scheduling for Airborne Radar - MEng placement at Leonardo UK(formerly Selex) - 2017
- Dongjin Kim - Waves that look like ship: characterising radar sea clutter - MSc project - 2017
- Matteo Pepa - Radar and Kinect sensors for automatic activity recognition - Erasmus+ Placement - Co-supervised with Prof Susanna Spinsante, Universita' Politecnica delle Marche - Feb-Jun 2017
- Aleksandar Angelov - Investigating novel machine learning algorithms to characterise the radar micro-Doppler signatures of people - Co-supervised with Prof Roderick Murray-Smith - EPSRC funded summer internship (2016)
Multistatic radar systems with a network of multiple transmitter and receiver nodes separated by significant distances can provide substantial advantages over conventional monostatic systems, such as additional information on targets by exploiting multi-perspective views from different radar nodes, and more resilience for the overall system in case of problems or jamming at one of the nodes.
Multistatic systems are however more complex to develop and to operate coherently in order to exploit Doppler information, and the performance is affected by the different geometries and deployment configurations of the nodes. There is currently a growing interest in the radar research community in multistatic systems, and it is expected that technological advances in the domain of signal processing, hardware miniaturisation, and increasing computational power can boost the development and application of these systems, e.g. nodes that become more and more intelligent and capable of adapting their operations to changes in the environment (cognitive radar), or smaller nodes that can be carried on autonomous vehicles (drones and UAVs for instance).
Research applications and activities include:
- Analysis of human micro-Doppler signatures extracted from radar data for classification of different activities performed by people (walking, running, carrying objects, crawling, and so on), as well as for the recognition of particular individuals from their walking gait. This can allow restricted areas at military and commercial sites to be monitored for unauthorised personnel to ensure security and safety, help identify potentially hostile behaviour (e.g. someone carrying weapons), and monitor for potential situations of distress (e.g. fall detection for elderly people).
Figure 1. Radar micro-Doppler signatures of four different people walking, which could be used for personnel recognition
- Detection and classification of micro-drones and small UAVs, in a context where these platforms are becoming more and more available for the wider public, providing opportunities for potential misuses and even criminal uses (privacy invasion, smuggling of illicit substances, attack with explosives or chemicals). Research applications include the analysis of Radar Cross Section and micro-Doppler signatures of these platforms for improved detection and classification, identification and potential discrimination of payloads carried by these platforms, reduction of false alarms caused by birds.
Figure 2. Radar range-time signature of a micro-UAV and feature samples to discriminate classes of micro-UAVs carrying different payloads.
- Automatic Target Recognition based on feature extraction and machine learning algorithms for classification. Research activities include the investigation of suitable signal processing techniques and features to extract from the radar data in different scenarios, algorithms for feature selections, algorithms for classification based on supervised and unsupervised learning techniques.
Figure 3. Feature samples extracted from radar data to identify 3 individuals from their walking gait.
- Through-wall radar imaging to improve the situational awareness of soldiers and law enforcement agents operating in urban contexts (e.g. breaking in a room with hostages and/or hostile personnel), and to support first responders in search and rescue operations (e.g. people trapped in burning buildings or buried under rubble).
- Clutter characterisation in multistatic geometries, as with multiple radar nodes some of them may receive clutter with more advantageous statistical properties, improving the detection performance of the radar system in terms of rejection of false alarms and increased sensitivity for target detection. An example is the detection of small boats and inflatables, often used for illicit activities such as smuggling, trafficking, and piracy, to be detected against the varying background of the sea (sea clutter).
- Wind farm clutter characterisation, with the aim of investigating whether multistatic radar can be more resilient to the adverse effect of wind farms on radar performance, as with multiple operational nodes some of them may be less affected by wind farm returns and still provide the required detection, tracking, and classification of targets.