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Activity title

Acoustic and Seismic Sensing of Threats in Urban Environments

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Sensors & Electronics Technology

Security Classification




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acoustic, artificial intelligence, buildings, classification, decision aid, distributed, electrooptic, field trial, heterogeneous, lidar, localization, machine learning, modelling, processing, propagation, seismic, sensors, shots, signatures, transient, urban, vehicles


An emerging theater of recent conflicts is that of urban areas. A highly complex mega-city/dense urban environment is the pacing threat considered in prospective scenarios for R&D guidelines in a number of NATO countries. Urban environments are complex, obscured propagation channels, and are thus a major challenge to sensing and situational awareness. Acoustic/seismic signals emitted by sniper shots, explosions, or vehicles are detectable, even outside the line-of-sight. This makes acoustics/seismics a method of choice for detecting and localizing such threats in urban areas. Other modalities (e.g. imaging) can then be cued to classify and identify the threat. It remains that urban obstacles adversely affect the acoustic/seismic signatures, and the subsequent performance of sensing systems. In the last decades, the SETs 093, 142, 189, and 233 investigated acoustic sensing on the battlefield. These efforts collected acoustic signatures of various threats (direct/indirect fires), with various sensors and sensing systems, in open environments. These efforts did not include the urban environments. On the other hand, the SET 153 explored situational awareness in urban operations with a variety of sensors, but the challenges and advantages of acoustic sensing in such environments were not fully investigated.


The first objective of the RTG is the collection of acoustic/seismic signatures in urban areas. The organization of a field experiment is a complex effort, especially since urban areas are to be considered, with sniper shots and explosions. Previous SETs show that a field trial, with participants bringing their sensors on a shared-data basis, is immensely valuable to all. Representative threats with known near-field signatures should be used. The propagation channel (urban environment) should be taken as representative as possible (height and density of buildings), and documented as best as possible. Acoustic sensors and antennas should be placed in and outside the line-of-sight. For security reasons, real urban activities may not be able take place during the field experiment; therefore, methods to characterize and include realistic background noise environments, e.g., via post-processing or broadcast of pre-recorded street-scapes, should be considered. A second objective of the RTG is testing of innovative sensing systems in urban scenarios. Distributed sensing (e.g., embedded processing at the sensor level, with information relay) and fusion with other sensing modalities (imaging, laser displacement monitoring, lidar, etc.) may be investigated. Artificial Intelligence and Machine Learning methods may be used to improve the Detection / Localization / Classification (DLC) of threats. Methods to ingest knowledge on the propagation channel may also be used to improve the DLC, or to optimize the sensors’ placement (e.g., tactical decision aid).


Sensor science: - Hardware, specifications for new threat assessment, - Characterize urban propagation channel effects on signatures, New concepts of sensing: - Distributed sensing + relay + fusion, decentralized (embedded) processing at sensor level - Machine Learning (ML) and Artificial Intelligence (AI) processing Source-to-sensor numerical modeling of signatures - Ingestion of urban terrain and its effects in signature predictions - Reliability and innovative use of the predicted signatures

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