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Data fusion and assimilation for scientific sensing and computing

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Applied Vehicle Technology

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big data, data assimilation, Data fusion, data mining, optimization


High-fidelity computer simulations are having an increased importance in design decisions for marine, ground, and air vehicles. As the sophistication of the models grows, so does the need for experimental data to develop new models and validate existing ones. Therefore, modern experiments have to monitor multiple measurands simultaneously and in a distributed manner. Furthermore, next generation computational models are being developed based on data assimilation instead of the traditional regression approach and require big data to train the algorithms. With the advancement of low-cost sensors and multi-sensor data fusion algorithms developed over the years by military (e.g. for situational awareness on the battlefield) and consumer industry (e.g. self-driving cars), one is on the verge of a paradigm shift in scientific instrumentation and computations. Traditionally, laboratory scales measurements rely on a single technique and, therefore, single physical process (temperature, velocity, concentration, etc.), resulting in idealized canonical experiments where real world effects are not present with very specialized instruments (i.e. expensive and complex because produced in small quantities). Network of multiple inexpensive sensors integrated with advanced algorithms could enable to bridge several gaps in traditional scientific instrumentation and enable data mining.


The goal of this ET is to provide the NATO community with information on the state of the art for data fusion and assimilation methodologies and translating this for applications to scientific experiment and computations. From this activity, communities will be built through the review of methods that are typically studied in specialized fields and the conduction of demonstrations. Demonstrations will include experiments, computations, and design of experiments with optimized deployment of sensors and assimilation of data into computational models.


• Inexpensive distributed multi-physics sensor network • Fusing with traditional scientific instruments • Reduction of data imperfection and sensor-fault detection • Data assimilation for computational models’ development • Optimization of sensors deployment and data transmission/handling/storage • Deployment in real-world platform to bridge scaling gap between experimental and real platforms • Distributed data processing, storage, and replication • Simulation, machine learning, surrogate modelling, and optimization

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