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Artificial Intelligence for Early Warning Intelligence and effective response for CBRN threats

The threats of chemical, biological, radiological, nuclear and explosive (CBRNE) hazards continue to advance. CBRN weapons are some of the most indiscriminate and deadly weapons in existence today, with capability to affect large population in wide geographical area and in short time. The release of Chemical, Biological, Radiological and Nuclear (CBRN) materials, whether deliberate or accidental, may have the potential to cause serious harm and severe disruption to the delivery of vital public services over a wide geographical area.

 

CBRN defence remains an indispensible part of the strategic security preparedness of all nations. The overarching goal of CBRN Defense measures is keeping CBRN environments from having adverse effect on personnel, equipment, critical assets and facilities. This includes providing the most advanced diagnostic equipment and countermeasure technology for identifying and protecting against imminent threats.

 

Companies are developing  capabilities to detect, attribute, deter, respond to and prevent terrorist attacks by modelling, assessing, analyzing, predicting, characterizing and deterring the full range of CBRNE threats. Techniques range from leveraging data fusion and analytics tools, including simulation, modeling, and knowledge management systems.

 

Early warning of an attack can save lives, but that’s easier said than done when you can’t see the attack coming, such as with chemical, biological, radiological, and nuclear (CBRN) agents.

 

AI

AI has an important role in mitigating global and national threats. AI is being used to fight the ongoing COVID-19 pandemic from screening, early detection, and diagnosis of the infection to containment, contact tracing, development of drugs and vaccines, and training of healthcare workers.

 

For instance, it speeded up the development of messenger ribonucleic acid (mRNA) based vaccines. Further it can accurately model the spread of epidemics and disease outbreaks, which allows health care to reach quickly to those in need.

 

AI technology can be used in the detection of CBRNE threats and protection from them, as well as in training and simulation. Facial recognition and behaviour recognition systems helps detect abnormal behaviours at important passengers’ checkpoints such as airports, train stations and ports.

 

For example, the Mount Sinai Health System has partnered with Sana Labs to train nurses treating Covid-19 patients using AI-enabled assessments. According to a GlobalData survey, 43% of respondents stated that AI had played a significant role in helping the company survive the pandemic, with a further 34% saying it had played a minor role.

 

Conversational platforms have become more important than ever following dramatic increases in demand for support services. The pandemic has also accelerated AI research in federated learning, which allows for collaboration on models without forcing users to disclose sensitive information.

 

In June 2021, the US Department of Homeland Security (DHS) Small Business Innovation Research (SBIR) Program awarded $2m funding to two small businesses to develop machine learning technologies for detection of CBRNE threats. DHS aims to reduce time, redundancy, cost, and improve accuracy in detecting threats, such as explosives, chemical agents, and narcotics.

 

Accelerating Integrated Early Warning for DoD’s CBRN Defense

To help the Department of Defense (DoD) detect these threats sooner, LMI delivered a rapid capability prototype that emulates new CBRN detection technologies in operational environments. The simulated CBRN threat detection prototype integrates unmanned aerial and ground vehicles, ground-based radar, and agent-specific sensors. The effort facilitates further concept development and demonstrations as well as testing, training, and field deployment of enhanced early-warning capabilities.

 

The prototype advances a concept called tipping and cueing—using reconnaissance assets to detect changes in an environment, then targeting areas of concern. Tipping and cueing is a foundational element of integrated early warning, according to Mark Malatesta, who leads LMI’s rapid capability development team.“Enhanced tipping-and-cueing capability enables us to act earlier than traditional methods,” he said. “Normally, we wait for a sensor to go off, then respond. Integrated early warning takes information from various sensors on the battlefield to achieve earlier warning. Even an extra 10 seconds may be the difference that warfighters need to put on gas masks during an attack.”

 

Among the deliverables was a first-of-its-kind radar emulator. Based on the AN/TPQ-50 Lightweight Counter Mortar Radar, the emulated capability will be used in demonstrations and training events. LMI also helped develop a series of software emulators based on the fixed-site FLIR Centaur System for CBRN detection. Users can model hazard scenarios and test how different combinations of detectors would perform in the field.

 

“The prototype enables DoD to see how emulated capabilities perform in conjunction with live sensors. Through displays in a common operating platform, leaders visualize how systems could support earlier CBRN warning and inform strategic decision-making,” Malatesta said. “These unique capabilities expand DoD’s understanding of CBRN defense and furnish a more holistic operational environment.”

 

With LMI’s approach, software and hardware developers were able to build interfaces in parallel, accelerating the testing timeline and reducing overall program costs for DoD’s Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense (JPEO-CBRND). In November 2019, LMI announced it will support another JPEO-CBRND effort, the Joint Health Risk Management toolkit, as it expands CBRND and counter-weapons of mass destruction expertise for new and current customers.

 

Early Warning Intelligence System could save lives in Chemical attacks

In 2017, Researchers from the University of Texas at San Antonio (UTSA) Laboratory of Turbulence Sensing & Intelligence Systems are taking a closer look at how turbulence diffuses chemicals in the event of a chemical weapons attack.

 

Funded by the US Department of Army Edgewood Chemical and Biological Center (ECBC), Kiran Bhaganagar and her team are developing an early warning prediction system using a weather research forecasting model (WRF) with local sensor data to predict air-borne release chemical plume. Local turbulence and the type of the gas dictates the direction of the plume path.

 

The intelligence system relies on solving an intricate set of thousands of mathematical equations and processing millions of data within a few minutes. This is done on supercomputing processing systems with 50,000 graphical core units working simultaneously to predict the plume path in real-time.

 

The team simulated the same conditions as a recent Syrian gas attack on a small town of Khan Shaykhun, in which as many as 100 people may have been killed. Using this intelligence system and local conditions in Khan Sheikhoun, the team was able to predict exactly how far and high the gas would spread, and at what speed. When they compared the simulation data to the actual details of the real attack, they found that they matched. The model worked and could realistically warn potential victims of a chemical attack to flee the area.

 

Bhaganagar’s study demonstrates that local wind and terrian conditions and atmospheric turbulence make chemical attacks even more deadly than previously understood, and proposes that analysis of the wind and the use of data-collecting drones could make for an early warning system that would allow people in potentially deadly areas to evacuate before the gas reaches them.

 

The challenge in developing the intelligence systems is to obtain the local wind, turbulence surface and chemical gas sensing data. Currently, the team is demonstrating using aerial drones that scan the region in the vicinity of the chemical source and get point-point sensing data.

 

“We are moving from traditional single-point stationary sensors to novel concept of mobile sensing which is low cost, fast collection of sensing data and very accurate,” said Bhaganagar. “This is the next step. We will deploy low-cost aerial drones to collect wind and gas concentration sensing data. We can alert people to danger within minutes.”

 

Dstl develops HASP modelling software for CBRN incidents

The UK’s Defence Science and Technology Laboratory (Dstl) said in 2019, that it has developed a new software called Hazard Assessment Simulation and Prediction (HASP) Suite to help in effective response to chemical, biological, radiological and nuclear (CBRN) incidents. The HASP software has been licensed to Riskaware by Ploughshare Innovations, a wholly owned subsidiary of Dstl. The software will help emergency responders and military commanders in reducing the risk to the public from CBRN events, thereby saving lives.

 

According to Dstl, the new software accurately models how hazardous materials released in cities, towns, and open areas are spread. Emergency responders will be helped by the models in predicting how any CBRN threat will disperse and enable them to manage a response in order to curb it and protect the people.

 

Ploughshare CEO James Kirby said: “We are pleased another Dstl innovation will be made available to industry and one which will improve the operational effectiveness of teams facing CBRN threats. This deal further demonstrates how Ploughshare maximises the MOD’s investment in Science & Technology by delivering capability to front-line services.” The HASP software, which has been in development for more than 20 years, offers hazard predictions in urban environments within minutes, which is a great improvement compared to previous models, said Dstl.

 

The new modelling software also takes into account the interactions between indoor and outdoor dispersion and also estimates the parameters of the source that include location, discharge time, and the quantity of substance released. It will be available as an independent product from Riskaware starting from this month and will also be available in EuroSIM CBRN, a next generation CBRN information management system.

 

Riskaware managing director John Bishop said: “The HASP Suite is an excellent capability and we are both proud and excited to be taking it to market. It will also support our goal to transform Riskware’s business from being a CBRN prediction software developer into a truly global company in CBRN information management.”

 

 

 

References and resources also include:

https://www.governmentcomputing.com/national-security/news/dstl-hasp-modelling-software

https://cbrnecentral.com/early-warning-intelligence-system-save-lives-chemical-attacks/10750/

 

 

 

Cite This Article

 
International Defense Security & Technology (October 6, 2022) Artificial Intelligence for Early Warning Intelligence and effective response for CBRN threats. Retrieved from https://idstch.com/threats/artificial-intelligence-for-early-warning-intelligence-and-effective-response-for-cbrn-threats/.
"Artificial Intelligence for Early Warning Intelligence and effective response for CBRN threats." International Defense Security & Technology - October 6, 2022, https://idstch.com/threats/artificial-intelligence-for-early-warning-intelligence-and-effective-response-for-cbrn-threats/
International Defense Security & Technology June 3, 2022 Artificial Intelligence for Early Warning Intelligence and effective response for CBRN threats., viewed October 6, 2022,<https://idstch.com/threats/artificial-intelligence-for-early-warning-intelligence-and-effective-response-for-cbrn-threats/>
International Defense Security & Technology - Artificial Intelligence for Early Warning Intelligence and effective response for CBRN threats. [Internet]. [Accessed October 6, 2022]. Available from: https://idstch.com/threats/artificial-intelligence-for-early-warning-intelligence-and-effective-response-for-cbrn-threats/
"Artificial Intelligence for Early Warning Intelligence and effective response for CBRN threats." International Defense Security & Technology - Accessed October 6, 2022. https://idstch.com/threats/artificial-intelligence-for-early-warning-intelligence-and-effective-response-for-cbrn-threats/
"Artificial Intelligence for Early Warning Intelligence and effective response for CBRN threats." International Defense Security & Technology [Online]. Available: https://idstch.com/threats/artificial-intelligence-for-early-warning-intelligence-and-effective-response-for-cbrn-threats/. [Accessed: October 6, 2022]

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