Category Archives: Sensing & electronics

Cloud profiling radar


CAD model of cloud radar showing transmit beam (yellow) and receive beam (blue).

Millimetre wave cloud profiling radar development

Dr Duncan A. Robertson  | Millimeter Wave & EPR Group,
School of Physics & Astronomy

Cloud profiling radars are used to determine the structure and extent of clouds, fog and aerosols and to generate data that can guide weather predictions.  Radars operating at millimetre wavelengths are ideal for this application as the particle sizes in clouds are such that radar measurements can be made successfully throughout the cloud structure. The aim of the project is to modify an existing low power solid-state millimetre wave radar to make it suitable for cloud profiling.

There is a newly emerging market for cloud profiling radars which are more affordable than traditional high power designs. We believe our architecture will achieve state‑of‑the‑art performance at substantially lower costs than alternative designs which, once demonstrated to stake holders (e.g. MetOffice), will make a convincing case for commercialisation.


Cloud radar electronics: transmitter (left)


and receive (right)


Environmental hazards monitoring


Radar Monitoring of Environmental Hazards in Montserrat

Dr Duncan Robertson & Dr David Macfarlane | Millimeter Wave & EPR Group,
School of Physics & Astronomy

Montserrat, a UK Overseas territory, is home to the UK’s only active volcano and island residents live with the hazards of pyroclastic flows from lava dome collapses, eruptive ash fall, and mud flow ‘lahars’ after heavy rain.

Our AVTIS millimetre wave radar has been deployed on Montserrat to monitor the volcano since 2011 but 5 years of exposure to tropical conditions has substantially degraded the associated infrastructure of the radar housing and wifi telemetry link.

In this project, we are refurbishing the radar installation and integrating the instrument into the Montserrat Volcano Observatory’s (MVO) operational monitoring suite of sensors, so it can provide round-the-clock data on the lava dome shape, and high temporal and spatial resolution rainfall mapping. MVO will use these data along with that from other sensors to provide enhanced hazard warning to the people and business of the island.


Radar reflectivity map of Soufrière Hills Volcano, Montserrat taken with AVTIS radar.

Explosives detection


An optical sensor platform for the detection of explosive remnants of war

In-field prototype optimisation and end-user engagement

Dr Ross Gillanders & Prof. Graham Turnbull  | Organic Semiconductor Centre,
School of Physics & Astronomy

Since legacy landmines affect local populations beyond the obvious dangers, there is worldwide interest in advanced Explosive Remnants of War (ERW)-detecting technology. Many countries are affected by mines dating from World War I to current conflicts.

Mined areas can inhibit

  • farming
  • trade &
  • communication between local communities.

The Ottawa Convention, seeking to ban land mines completely, has 136 countries as signatories. However, humanitarian demining is typically a time-consuming and expensive process.

Cutting-edge technologies can help improve these issues.

At the Organic Semiconductor Centre, we have developed thin polymer films that emit light.  When the films come into contact with explosive vapour this light dims. By exploiting this phenomenon, we were able to develop, during the Tiramisu FP7 project, a portable system to transport the technology from the lab to the field. However, trace vapour sampling in the field is a huge challenge.

The work funded by the EPSRC Impact Acceleration Award grant is improving our previously developed prototype to make it truly suitable for deployment in mine-afflicted areas by introducing a “pre-concentration” stage to increase the level of explosive vapours exposed to the sensor. Having a fast-responding, lightweight, inexpensive sensor system could significantly impact deminers’ toolkits and methods, and so significantly help clearance efforts.

Making use of laser speckle

Measurement of wavelength using laser speckle   

Prof. Kishan Dholakia | School of Physics & Astronomy

Light is a wave and it is well known waves can interfere creating patterns with constructive (bright) and destructive (dark) regions. For a laser this means we can create a granular pattern – a speckle pattern- that is rich in information about the incoming waves. We show how we can use this pattern from a laser as a signature to measure the laser wavelength to high accuracy and use it to remove laser fluctuations and thus stabilise a laser. This has applications for laser measurement, spectroscopy and stabilising lasers for quantum applications.

Millimetre antennas


An array of 16 finished horns for airport security imaging radar

High performance, low cost, antennas in the microwave to THz regime   

Dr Graham Smith & Dr Duncan Robertson | Millimeter Wave & EPR Group,
School of Physics & Astronomy

Horn antennas at microwave, mm-wave and terahertz frequencies are commonly used in high performance radars, radiometers and in advanced instrumentation and communication systems. Increasingly, these applications require highly directional, strongly polarised beams to be produced by very compact, low weight antennas, especially for spaceborne use.

In this project, we are developing a generic design tool which provides a simple and flexible methodology to design compact horns with very high levels of performance (e.g. low sidelobe level, low cross-polar level over wide bandwidths) at low cost. We have developed and tested a number of high performance designs, we are exploring novel low cost manufacturing methods, and we are working with manufacturers and microwave component suppliers targeting a number of application areas, including upcoming space missions.


Measured near field scans of the beam from the submillimeter wave split block feedhorns, co-polarised (left)


and cross-polarised (right)


Robot behaviour

How can robot behaviour be safely unscripted?

Dr Michael K. Weir  | School of Computer Science

As robots move into more uncontrolled environments, safe motion requires flexibility to deal with the unexpected. In this research, scripted behaviours and their limitations in robots are compared with unscripted designs. The NAO humanoid robot for instance has a good example of a scripted language and a wide ranging physical capability for such comparison. The aim is to investigate ways in which moves can be made towards motion on an unscripted basis with less limitations which is also safe.

Water pollutant detection


AquaCat: Using Radar and Machine Learning for water pollutant detection

Dr David Harris-Birtill & Mr David Morrison  | School of Computer Science

The AquaCat project makes use of low-cost miniaturized radar technology and machine learning in order to reliably detect water pollutants.  It is a collaboration between the University of St Andrews in Scotland and the Universidade Federal de Goiás in Brazil.  The project builds on RadarCat, a radar-based object classification system developed by the University of St Andrews School of Computing’s Human-Computer Interaction Group (SACHI) [1,2].

Brazil faces unprecedented problems caused by water pollution.  Untreated sewage, industrial chemicals, and agricultural waste have entered the waterways introducing pollutants dangerous to both humans and the environment. For example, dangerous levels of Thallium, Arsenic and Zinc have been found in the Pardo River in São Paulo [3].

Current sampling and analysis techniques, such as inductively coupled plasma mass spectrometry (ICPMS), require expensive specialist equipment, reducing the possible scale of measurement, both in time and in space. We are addressing this issue by producing a small, low-cost and portable pollutant detector based on miniaturized radar technology.

Our long-term plan is to allow environmental researchers to make cheaper, faster and thus more numerous measurements in the field and therefore create a richer data set.  When miniaturised radar technologies are deployed into smartphones and other devices, AquaCat could be available to a much larger section of the population.  This will enable the collection of crowd-sourced pollution data over a large geographic area and over a long time.  By having a higher number of samples over a longer period, policymakers will gain a better understanding of the distribution of pollutants and their sources, allowing them to better target regulatory interventions and infrastructure improvements.

[1] Object Recognition with the Project Soli in St Andrews:

[2] Hui Y, Flamich G., Schrempf P, Harris-Birtill D., Quigley A. RadarCat: Radar Categorization for Input & Interaction, submitted to ACM UIST 2016, Tokyo Japan (under review)

[3] Alves, Renato IS, et al. “Metal concentrations in surface water and sediments from Pardo River, Brazil: human health risks.” Environmental research 133 (2014): 149155.