This paper presents a sound source localization strategy that relies on a microphone array embedded in an unmanned ground vehicle and an asynchronous close-talking microphone near the operator. A signal coarse alignment strategy is combined with a time-domain acoustic echo cancellation algorithm to estimate a time-frequency ideal ratio mask to isolate the target speech from interferences and environmental noise. This allows selective sound source localization, and provides the robot with the direction of arrival of sound from the active operator, which enables rich interaction in noisy scenarios. Results demonstrate an average angle error of 4 degrees and an accuracy within 5 degrees of 95\% at a signal-to-noise ratio of 1dB, which is significantly superior to the state-of-the-art localization methods.

Unmanned aerial vehicles (UAVs) are critical in the automated inspection of wind turbine blades. Nevertheless, several issues persist in this domain. Firstly, existing inspection platforms encounter challenges in meeting the demands of automated inspection tasks and scenarios. Moreover, current blade stop angle estimation methods are vulnerable to environmental factors, restricting their robustness. Additionally, there is an absence of real-time blade detail prioritized exposure adjustment during capture, where lost details cannot be restored through post-optimization. To address these challenges, we introduce a platform and two approaches. Initially, a UAV inspection platform is presented to meet the automated inspection requirements. Subsequently, a Fermat point based blade stop angle estimation approach is introduced, achieving higher precision and success rates. Finally, we propose a blade detail prioritized exposure adjustment approach to ensure appropriate brightness and preserve details during image capture. Extensive tests, comprising over 120 flights across 10 wind turbine models in 5 operational wind farms, validate the effectiveness of the proposed approaches in enhancing inspection autonomy.

-- Autonomous tracking of flying aerial objects has important civilian and defense applications, ranging from search and rescue to counter-unmanned aerial systems (counter-UAS). Ground based tracking requires setting up infrastructure, could be range limited, and may not be feasible in remote areas, crowded cities or in dense vegetation areas. Vision based active tracking of aerial objects from another airborne vehicle, e.g., a chaser unmanned aerial vehicle (UA V), promises to fill this important gap, along with serving aerial coordination use cases. Vision-based active tracking by a UA V entails solving two coupled problems: 1) compute-efficient and accurate (target) object detection and target state estimation; and 2) maneuver decisions to ensure that the target remains in the field of view in the future time-steps and favorably positioned for continued detection. As a solution to the first problem, this paper presents a novel integration of standard deep learning based architectures with Kernelized Correlation Filter (KCF) to achieve compute-efficient object detection without compromising accuracy, unlike standalone learning or filtering approaches. The proposed perception framework is validated using a lab-scale setup. For the second problem, to obviate the linearity assumptions and background variations limiting effectiveness of the traditional controllers, we present the use of reinforcement learning to train a neuro-controller for fast computation of velocity maneuvers. New state space, action space and reward formulations are developed for this purpose, and training is performed in simulation using AirSim. The trained model is also tested in AirSim with respect to complex target maneuvers, and is found to outperform a baseline PID control in terms of tracking up-time and average distance maintained (from the target) during tracking. Vision based air-to-air tracking of an aerial object is an important and challenging problem with potential applications spanning collision avoidance, multi-aircraft coordination, and counter unmanned aerial system (counter-UAS) scenarios [1], [2]. In such applications, the ability to track the flying object from another autonomous aircraft or uncrewed aerial vehicle (UA V) - the chaser - would expand operational capabilities, in terms of area, speed and agility of tracking and enable quickness of response that might otherwise be difficult with ground based tracking [3].

Small racing quadcopters carrying explosives, known as first-person-view drones or FPVs, have become the dominant weapon in the war in Ukraine. Now, some are fitted with solar cells so they can wait for extended periods to ambush targets, turning them into a new type of land mine. "The drone can sit by a road or choke point and when it acquires its target, it can then do a quick sprint to the target," says Robert Bunker at US consultancy firm C/O Futures. Drone ambushes, where the devices land beside a road or on a building and wait for a target, are already commonly carried out by both Russian and Ukrainian forces. But even with their engines turned off, their camera and radio communications drain the drones' battery, limiting waiting time to a few hours at best.

Neither President Franklin Delano Roosevelt nor British Prime Minister Winston Churchill, nor any of their senior military or political advisors, saw the Japanese attacks of late 1941 coming. The forces of Imperial Japan achieved total surprise across the Pacific. The intelligence failures in the U.S. leading up to Pearl Harbor were catastrophic. So was Great Britain's general underestimation of the threat from Imperial Japan. The U.K.'s fortress outpost in the Pacific at Singapore was thought to be, if not impregnable, than as close to it as possible.

This work introduces a self-supervised neuro-analytical, cost efficient, model for visual-based quadrotor control in which a small 1.7M parameters student ConvNet learns automatically from an analytical teacher, an improved image-based visual servoing (IBVS) controller . Our IBVS system solves numerical instabilities by reducing the classical visual servoing equations and enabling efficient stable image feature detection. Through knowledge distillation, the student model achieves 11 faster inference compared to the teacher IBVS pipeline, while demonstrating similar control accuracy at a significantly lower computational and memory cost. Our vision-only self-supervised neuro-analytic control, enables quadrotor orientation and movement without requiring explicit geometric models or fiducial markers. The proposed methodology leverages simulation-to-reality transfer learning and is validated on a small drone platform in GPS-denied indoor environments. Our key contributions include: (1) an analytical IBVS teacher that solves numerical instabilities inherent in classical approaches, (2) a two-stage segmentation pipeline combining YOLOv11 with a U-Net-based mask splitter for robust anterior-posterior vehicle segmentation to correctly estimate the orientation of the target, and (3) an efficient knowledge distillation dual-path system, which transfers geometric visual servoing capabilities from the analytical IBVS teacher to a compact and small student neural network that outperforms the teacher, while being suitable for real-time onboard deployment.

A Russian drone attack on a 25-storey residential building in Ukraine's capital, Kyiv, injured eight people, including a four-year-old girl, the head of the city's military administration, Tymur Tkachenko, said. The overnight attack was part of a barrage of "324 drones, four cruise missiles and three ballistic missiles", across the country, the Ukrainian Air Force said. The attack was focused on Starokostiantyniv, home to a major air base, the Air Force added. Ukraine's Air Force said it downed 309 drones and two missiles, while 15 drones and two missiles hit targets in three locations, without specifying where. The attack started a fire in Kropyvnytskyi, in central Ukraine, local officials said, but no injuries were reported.

DroneUp CEO Tom Walker speaks with Fox News Digital about his Congressional testimony calling for a nationalized database of drone pilots and flights amid changing technology, while warning the country's airspace regulations are unprepared. As drone technology rapidly advances, industry experts are warning Congress about potential airspace lapses creating the next national security threat if left unregulated. In a U.S. House Homeland Security Subcommittee hearing held last week, drone industry experts testified about the looming threats to airspace safety posed by unmanned aircraft systems (UAS). "More than half of all near misses with commercial and general aviation are with drones," Tom Walker, CEO of DroneUp, told Fox News Digital. Drone experts are asking Congress for a centralized database to track flights and pilots in an attempt to fill gaps in airspace regulations.

Russian forces attacked Ukraine's capital, Kyiv, early on Monday, wounding five people and damaging a residential building, according to the head of the city's military administration, Tymur Tkachenko. A Russian drone hit a Ukrainian bus carrying 39 evacuees in the eastern Sumy region, near Ukraine's border with Russia, on Sunday, killing three people and wounding 19 others, according to the regional governor. Two others were killed in a landmine explosion in Sumy's Esman community on Saturday, while two more were killed in Russian attacks on the front-line Donetsk region, according to officials, taking the death toll from attacks across Ukraine on that day to at least six. Ukraine's forces also launched drone attacks at Russia on Sunday, with the governor of the Leningrad region reporting that at least 10 Ukrainian unmanned aircraft were downed over the areas surrounding the city of St Petersburg. Falling debris injured a woman, Governor Alexander Drozdenko said.

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.