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UK MOD’s innovation initiative includes creating civil-military synergies, British DARPA and increasing research capacity and business innovation

In a major speech at the University of Oxford, the Defence Secretary announced the formation of a world class panel which will help challenge and drive the Ministry of Defence’s innovation agenda. The Panel will be charged with driving forward the MOD’s Innovation Initiative, which aims to encourage imagination, ingenuity and entrepreneurship, in pursuit of maintaining a military advantage in the future.

Defence Secretary Sir Michael Fallon said: Backed by an £800M innovation fund, we want to transform defence and work with small firms, academics and others to find solutions to the threats to our security. This panel of world class innovators will bring their drive and expert insights to the vital task of keeping the UK ahead of our adversaries and boosting our prosperity.

The UK government will spend an extra £2 billion a year by 2020-21– a 20 per cent increase in government research and development spending- on collaboration between business and scientists as well as creating a technology fund modelled on the Defense Advanced Research Projects Agency (Darpa).

The extra £2 billion ,  will be distributed by UK research councils and Innovate UK in two funding streams. The first is an “Industrial Strategy Challenge Fund”, a cross-disciplinary fund that will “support collaborations between business and the UK’s science base, which will set identifiable challenges for UK researchers to tackle”. Based on Darpa, the fund will back technologies “decided by an evidence-based process”.

There will also be a second stream of funding to “increase research capacity and business innovation, to further support the UK’s world-leading research base and to unlock its full potential”. The UK’s new research body, UK Research and Innovation (UKRI), will award this funding on the basis of “national excellence”, while there will also be more grant funding for Innovate UK.

Sarah Main, director of the Campaign for Science and Engineering (CaSE), said that the announcement was “truly exciting”. “To stay cutting edge, it will be vital that balance is maintained between discovery-led and challenge-led programmes, but I am encouraged that these decisions will rest with UKRI,” she said. Alice Gast, president of Imperial College London, said that the extra money showed that the chancellor had “recognised that investing in research and innovation is the best way to raise productivity”.

UK MOD wants to tap commercial Innovation in areas such as big data, analytics, autonomy and robotics. In December 2013, the European Council itself tasked the European Defence Agency and other bodies to better exploit civil-military synergies. The European council suggested “Desegmentation of civil and military research”, by allowing funding to flow from one side to the other, major spin-offs between defence and civil research could be achieved. “It is worth remembering that few technologies are military or civil by nature, especially at low technological readiness.

SMEs are at the heart of the Innovation Initiative, the goal is to work more effectively with businesses and academics across the UK – and particularly with SMEs which might not normally think of themselves as Defence suppliers.

During his speech, the Defence Secretary also announced the launch of a two year £8 million second phase of innovative research and development, exploring the future of unmanned air systems. Developed in partnership with Leonardo Helicopters, the Rotary Wing Unmanned Air Systems (RWUAS) Capability Concept Demonstrator will be a vital tool in discovering how unmanned air systems can support our personnel on the battlefield of the future by developing new concepts and technologies.


Innovation Initiative to bring future-tech and ideas to the Armed Forces

UK  government has launched  new defence innovation initiative to transform how defence deals with the challenges of tomorrow, and to gain critical advantage  defence and security forces. “An Innovation and Research Insights Unit (IRIS) will anticipate emerging trends in technology and analyse the implications for UK Defence and Security, informing critical decisions to maintain our military advantage and protect the UK.” Some of the technologies being considered are Surveillance drones inspired by dragonflies, laser weapons, mobile robots that can inspect incidents involving chemical materials, sensors that use gravity to survey underground structures in minutes, and virtual reality helmets to practice calling in simulated air strikes.

The Ministry of Defence currently spends up to 20% of its Science and Technology budget on cutting edge “disruptive capability” projects. Disruptive capabilities displace an established technology and shake up industry.

Current projects include:

A tiny Unmanned Aerial System with flapping wings inspired by the biology of a dragonfly, currently in development with Animal Dynamics. The ‘micro-drone’ will use cutting edge micro-engineering for unparalleled levels of performance. This has the potential to have a huge impact on intelligence-gathering in future operations in complex urban environments.

A new Quantum Gravimeter developed with the University of Birmingham could allow us to survey underground structures in minutes rather than weeks. This portable gravity sensing system uses cold atom quantum technology and two gravimeters coupled together for the first time to allow for higher sensitivity and reliability when carrying out surveys, enhanced robustness to external noise sources and drastically reduced measurement time. Applications for our Armed Forces range from spotting enemy tunnels to supporting disaster relief.

And we are developing a capability demonstrator with industry that will investigate the potential of laser weapons to target and defeat aerial threats.


UK MOD £10 million Innovation Challenge

UK MOD launched the £10 million Innovation Challenge in March 2015 with the aim of encouraging the development of innovative defence products. The MOD’s Centre for Defence Enterprise (CDE) in partnership with the DGP’s UK Defence Solutions Centre (UKDSC) ran the series of 4 SBRI themed competitions against which industry and academia for their innovative ideas and bid for funding.

Richard Brooks, Delivery Director at the Defence Science and Technology Laboratory (Dstl), said: “UKDSC, working closely with Dstl, has identified themes where significant challenges exist for defence and there are attractive future export markets. The competitions are looking for innovative proof-of-concept research proposals for solutions against these themes.”

The first 2 themed competitions are on the subjects of training and persistent surveillance. The MOD announced the theme of autonomy and big data for its next DGP Innovation Challenge, also compliant with SBRI. This competition, worth a total of at least £4 million over 2 phases.


Autonomy and Big data

“The amount of data both required and produced by defence systems and processes is rapidly increasing and becoming more difficult to manage. In a time when military manpower is limited, manual processing of data is too time consuming. The use of autonomous systems and processes to make sense of data to support decision making could increase efficiency and reduce the risk and cost of operations,” says UK govt website.

“Big data is a data set that is too large and complex to manage and process with standard methods or tools. This could be due to high data volume (amount of data), velocity (speed of data in and out) or variety (range of data types and sources). These issues apply equally for defence and commercial data.” Given the nature of the military decisions that have to be made, it’s of extreme importance that we understand the quality of the data and the value each piece of data brings.


UK MOD technical challenges

UK MOD identified four technology challenges for this theme

Challenge 1: acquiring data for autonomous vehicles

“The majority of autonomous systems being developed, particularly in civil applications, rely on comprehensive, detailed terrain and environmental data, known as ‘prior knowledge’. Locally sensed data is added to this for their navigation and safe operations.”

MOD will need to use autonomous vehicles in complex environments in the air, on land and at sea where there’s limited or out-of-date prior knowledge about the terrain or environment. The autonomous vehicles will have on-board sensors but these may be limited by the need to be covert or to operate in harsh electro-magnetic environments, eg GPS degraded or denied environments.

We’re looking for novel data collection methods (sensors, algorithms etc), which take in existing data from various sources, including potentially new sources or existing sources used in a novel way. We’re also looking for methods for gathering data from on board the autonomous vehicle, so that the data can be combined to allow effective and safe operation.


In challenge 1, we’re looking for methods of:

acquiring prior knowledge of environments to allow effective and safe operation of autonomous air, land, or sea vehicles processing data sets to simplify or enable their use by autonomous vehicles exploiting prior knowledge to achieve greater levels of autonomy. You should consider that the technologies may need to operate in complex and hostile environments, be used remotely or covertly, and at short notice.

We’re interested in technologies that allow a vehicle to: automatically select the best route in a complex and rapidly changing environment, eg due to the weather or battlespace activity, use passive rather than active sensors, operate without GPS and operate over extended ranges eg beyond visual line of sight communications.

We’re not looking for in challenge 1: platform technologies, eg airframes, propulsion; architectures; intent or threat profiling.


Challenge 2: sourcing big data in difficult environments

In any deployed task group, the command platform can be overwhelmed by data from various sources.

“When operating in a remote, isolated location in a hostile environment there’s a need to establish a high degree of confidence in the data received. Data for tactical decision making will need to be rapidly brought together from various sources and collection methods. Resources to address this, in terms of both computing and manpower, can be limited. Bandwidth limitations will affect the speed of accessing external data and there’ll be occasional loss of access to individual external data sources. Firewalls and other security restrictions may also limit data and information access.

Without relying on well-connected data centres, MOD will need to check data collected in a tactical environment against all the information available. This will be challenging due to the volume, variety and speed of the data, and the fact that it may not be known what data exists.

MOD will need to assign priorities and understand what’s critical to the mission. Given the diversity and sensitivity of some of the data sources, the security and integrity of the wider data sources will need to be maintained.

In challenge 2, we’re looking for proposals for technologies that allow storage, indexing, search, discovery, retrieval and visualisation of data collected that don’t rely on well-connected data centres.

Proposals should also consider the authentication and security aspects of the data queries.


Challenge 3: validating sources of big data

Data sources will include known, trusted and classified sources as well as ambiguous or unknown, and unvalidated open sources. The combination of this data to support decision making will require a confidence level or reliability score to be assigned.

This will also allow examination for audit, legal or ethical reasons, especially in circumstances where the rules of engagement for data collection and use may be dynamic. This requirement ranges from source validation, fact checking and confirmation, copyright assurance, through to protection of personal data and reputation management.

In challenge 3, we want proposals to develop novel tools, techniques, and procedures that combine data resources with metadata, or similar ‘tag’ constructs. These should clearly and concisely frame the way in which the underlying data may be considered, placed into context, and used. Proposals should consider the methods that the analytical, and other, components will use to interact with, and account for, the tagged data.


Challenge 4: managing and visualising big data

New persistent surveillance systems will produce significant volumes of data, likely to overwhelm the specialist surveillance analysts processing the data at intelligence centres. The problem is even more difficult when there are multiple, diverse sensor feeds from the same or multiple persistent surveillance systems. Many information types can be involved, ranging from traditional intelligence surveillance and reconnaissance (ISR) sources through to open-source and social media.

This can be solved by 2 approaches: near real-time processing close to or at the sensor, and post-processing analysis close to, or at, the analyst.

In challenge 4, we want proposals that develop novel tools, techniques and procedures to autonomously process, infer meaning from, and distribute information so that analysts aren’t overwhelmed by the data.

Given the complexity of this challenge, and the requirement for short-duration phase-1 proposals in this competition, we expect proposals to focus on component technologies or processes rather than address the challenge as a whole.


Winners of first round

The winners of the first round of these competitions were announced in September 2015

  1. Atlas ElektroniK UK Limited: Topological data analysis for robust autonomous situational awareness
  2. Autonomous Devices Limited: A real-time interactive query language for extracting intelligence from big data
  3. BMT Defence Services: Validating sources of big data – Learning to trust the crowd
  4. CountingLab Ltd: Trustworthiness of information from big data sets: An autonomous approach based on the analysis of the messages’ metadata, linguistic and spread through social networks
  5. Createc Live Maps: Air
  6. Cubica Technology Ltd: Automatic verification and fusion of open-source intelligence for regional situational awareness
  7. Exa Informatics: Knowledge based autonomous big data validation
  8. HORIBA MIRA: Real time UGV localisation using geo-referencing derived from deep learning applied to disparate data sets
  9. Mass Consultants Ltd: TimeSets: Timeline visualisation for provenance-based big data sensemaking
  10. Massive Analytic Limited: Artificial precognition and decision-making support for persistent surveillance-based tactical support
  11. Oxbotica Ltd: Experience based localisation – feasibility study
  12. Polaris Consulting Ltd: A risk based meta heuristic model for real time route optimisation in autonomous surface vehicles
  13. Polaris Consulting Ltd: Maritime autonomous navigation and mission effectiveness in GPS limited environments
  14. Polaris Consulting Ltd: Applying a dominant rough set approach to improve intelligence data and processing and enhance situational awareness
  15. Qinetiq Extracting visual navigation data from open source imagery
  16. Roke Manor Research: Reducing the workload of an Intelligence Analyst by automatically determining confidence in intelligence from the provenance of underlying data
  17. Selex ES: Linguistic summarisation of data to reduce data deluge
  18. SimCentric Limited: VSALT: visual search and autonomous linkage tool
  19. Stormcharge Ltd: Command and control of land and airborne autonomous vehicle groups in changing environment
  20. Swarm Systems Ltd: Fusion and synthetic environment for autonomous systems (FUSE)
  21. The University of Sheffield: Hypothesis generation and visualisation from big data
  22. The University of Sheffield: Bloom Filters for localised intelligence
  23. TRW Limited Dynamic generation of 3D perspective using array of descending cameras
  24. Vectis Environmental Consulting Team LLP: Position acquisition through statistical environmental recognition (PASER)


References and Resources also include:


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