Prior work has demonstrated that incorporating well-known quantum tunnelling (QT) probability into neural network models effectively captures important nuances of human perception, particularly in the recognition of ambiguous objects and sentiment analysis. In this paper, we employ novel QT-based neural networks and assess their effectiveness in distinguishing customised CIFAR-format images of military and civilian vehicles, as well as sentiment, using a proprietary military-specific vocabulary. We suggest that QT-based models can enhance multimodal AI applications in battlefield scenarios, particularly within human-operated drone warfare contexts, imbuing AI with certain traits of human reasoning.

The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This paper aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the non-robust estimators show a relative bias value $65$-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about $96\%$ and $10\%$, respectively, in the mean absolute value of both measures, in compassion to the non-robust estimators. Moreover, two SAR data sets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.

In the snowy streets of the north Ukrainian town of Trostyanets, the Russian missile system fires rockets every second. Tanks and military vehicles are parked on either side of the blasting artillery system, positioned among houses and near the town's railway system. The weapon is not working alone, though. Hovering tens of meters above it and recording the assault is a Ukrainian drone. The drone isn't a sophisticated military system, but a small, commercial machine that anyone can buy.

PARIS, (BM) – Two French companies are launching an ambitious project to master cyberspace, big data, and artificial intelligence. Thales and Atos are launching a joint venture to develop and offer technology in this area for both military and civilian purposes. The Joint Undertaking aims first to master the technologies for the internal market and focus its efforts on Europe at a later stage. On Thursday, 27 May, both companies issued statements announcing the joint venture merged under the Athea platform – a "sovereign platform for big data and artificial intelligence." According to the two companies, the countries in Europe and, respectively, the government structures of each country are sweating the need for technology to manage a large amount of data – something that is currently lacking.

Computer vision based AI models are being deployed in military contexts. The use of AI in weapon systems is a complex and controversial area, the practical and moral implications of which are still being debated by thought leaders and policy makers. However, AI powered military technology is increasingly being harnessed in order to protect service personnel operating in potentially dangerous situations and environments. By carrying out tasks that would ordinarily require risks to human life military AI applications are helping to protect soldiers and civilians alike. In order to interpret and function well within complex, dynamic environments military computer vision systems require varied and responsive image and video training data.

Futuristic hybrid drones with both a helicopter and airplane mode are set to revolutionise warfare, experts claim. Engineers have unveiled an ambitious new concept for adaptable unmanned aerial vehicles (UAVs) which are so agile they can take off and land like a helicopter and still fly like plane. Experts believe the drones of the future, which will alternate between fixed-wing flight and rotary-wing flight, could be deployed by soldiers in the next few decades. The new technology concept – named Adaptable UAVs – can alternate between the two different flight modes in the same mission. When in rotary wing mode the UAVs can be launched and recovered from battlefields and can also hover and achieve vertical take-off and landing.

Aerial drones get all the attention, but a new terrestrial drone named the Pegasus:Multiscope is an autonomous treaded vehicle that its makers call "the first unmanned ground vehicle (UGV) for off-road use." Use cases for the Pegasus:Multiscope include surveying challenging terrain for civil engineering projects or agriculture, or in hazardous areas such as near nuclear power stations or in conflict zones. The UGV's treads reduce ground pressure at any one point, allowing the vehicle, which weighs just under 2000 pounds, to traverse any type of terrain, including mud, sand or snow. Contractor Oshkosh Defense designs solutions to turn existing military vehicles into UGV.