From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relative distance measurements between the agents and the targets can be obtained by using onboard sensors. Based on the measurements, the center of all the targets is estimated directly by a fuzzy wavelet neural network (FWNN) and the least squares fit method. Then, a new distributed anti-synchronization controller (DASC) is designed so that the two tasking agents are able to encircle all targets while staying opposite to each other. In particular, the radius of the desired encirclement trajectory can be dynamically determined to avoid potential collisions between the two agents and all targets. Based on the Lyapunov stability analysis method, the convergence proofs of the neural network prediction error, the target-center position estimation error, and the controller error are addressed respectively. Finally, both numerical simulations and UAV flight experiments are conducted to demonstrate the validity of the encirclement algorithms. The flight tests recorded video and other simulation results can be found in https://youtu.be/B8uTorBNrl4.

Moldova has handed a note of protest to the Russian ambassador to Chisinau over alleged interference in its recent elections. The foreign ministry in Chisinau said in a statement on Tuesday that it turned over the "note of firm protest" in relation to the "illegal and deliberate interference" to envoy Oleg Ozerov during a meeting at its offices. Moldova has accused Russia of seeking to influence its recent presidential election and referendum on joining the European Union. Russia sought to affect results and delegitimise the democratic process, the ministry complained. Chisinau accused Russia of organising ineligible voting, bribery, and security threats in a bid to influence the votes.

Amid a rising tide of low-cost weaponized adversary drones menacing American troops abroad, the US military is pulling out all the stops to protect its forces from the ever-present threat of death from above. But between expensive munitions, futuristic but complicated directed energy weapons, and its own growing drone arsenal, the Pentagon is increasingly eyeing an elegantly simple solution to its growing drone problem: reinventing the gun. At the Technology Readiness Experimentation (T-REX) event in August, the US Defense Department tested an artificial intelligence-enabled autonomous robotic gun system developed by fledgling defense contractor Allen Control Systems dubbed the "Bullfrog." Consisting of a 7.62-mm M240 machine gun mounted on a specially designed rotating turret outfitted with an electro-optical sensor, proprietary AI, and computer vision software, the Bullfrog was designed to deliver small arms fire on drone targets with far more precision than the average US service member can achieve with a standard-issue weapon like the M4 carbine or next-generation XM7 rifle. Indeed, footage of the Bullfrog in action published by ACS shows the truck-mounted system locking onto small drones and knocking them out of the sky with just a few shots.

Search-based motion planning algorithms have been widely utilized for unmanned aerial vehicles (UAVs). However, deploying these algorithms on real UAVs faces challenges due to limited onboard computational resources. The algorithms struggle to find solutions in high-dimensional search spaces and require considerable time to ensure that the trajectories are dynamically feasible. This paper incorporates the lazy search concept into search-based planning algorithms to address the critical issue of real-time planning for collision-free and dynamically feasible trajectories on UAVs. We demonstrate that the lazy search motion planning algorithm can efficiently find optimal trajectories and significantly improve computational efficiency.

Scores of new witnesses have emerged with more footage of the eerie'drone' UFO swarms buzzing key US military sites, including'a big fireball in a cube' over Area 51. The Las Vegas-area witness who reported this bizarre cube-shaped object claims to have observed similar strange aerial lights in the area'over 100 times' since June 2020, adding that these craft'always seem to head towards Nellis Air Force base.' Nevada's Nellis base and its sprawling complex about 40 miles northwest of Vegas -- including top secret Area 51, now legendary within UFO lore -- appear to have faced incursions by craft similar to those that plagued the Air Force in Virginia. For at least 17 nights last December, swarms of noisy small UFOs were seen'moving at rapid speeds' and displaying'flashing red, green, and white lights' within the highly restricted airspace over Virginia's Joint Base Langley–Eustis. Vegas natives have posted videos confirming they too have seen more than one red, green or white UFO that'wasn't flashing like a regular aircraft [or] like a satellite.' Another witness, who documented one September 4, 2024 case from their own 60-night experience with the odd lights, hoped coming forward might help get answers.

Russia and Ukraine have both launched record drone attacks on each other overnight, with Ukrainian attacks on Moscow temporarily shutting down three of the Russian capital's airports. Russia fired 145 drones at Ukraine overnight, Ukrainian President Volodymyr Zelenskyy said on Sunday – more than in any single nighttime attack so far during their two-and-a-half-year conflict. "Last night, Russia launched a record 145 Shaheds and other strike drones against Ukraine," Zelenskyy said on social media, urging Kyiv's Western allies to do more to help Ukraine's defence. Kyiv said its air defences downed 62 of the drones. Russia also said it had downed 34 Ukrainian attack drones targeting Moscow on Sunday, the largest attempted attack on the capital since the start of the offensive in 2022, with Moscow regional Governor Andrei Vorobyov calling the attack "massive".

Ukraine's attempted strike on Moscow was reportedly its largest attack on the capital since the war began, and was described as "massive" by the region's governor. One person was reported injured as drones were shot down near the Russian capital. Images on social media showed a residential building on fire. Most of the drones were downed in the Ramenskoye, Kolomna and Domodedovo districts, officials said. In September a woman was killed in a drone attack that hit Ramenskoye.

Intelligent aerial platforms such as Unmanned Aerial Vehicles (UAVs) are expected to revolutionize various fields, including transportation, traffic management, field monitoring, industrial production, and agricultural management. Among these, precise control is a critical task that determines the performance and capabilities of UAV systems. However, current research primarily focuses on trajectory tracking and minimizing flight errors, with limited attention to improving flight time. In this paper, we propose a Model Predictive Control (MPC) approach aimed at minimizing flight time while addressing the limitations of the commonly used classical MPC controllers. Furthermore, the MPC method and its application for UAV control are presented in detail. Finally, the results demonstrate that the proposed controller outperforms the standard MPC in terms of efficiency. Moreover, this approach shows potential to become a foundation for integrating intelligent algorithms into basic controllers.

North Korean troops are said to have clashed with Ukrainian forces in the Russian region of Kursk for the first time on Tuesday, the same day American voters re-elected Donald Trump for president, an isolationist who has argued against sending further military aid to Ukraine. "The first battles with North Korean soldiers open a new page of instability in the world," said Ukrainian President Volodymyr Zelenskyy in his evening address. "We must do everything to make this Russian step to expand the war – to really escalate it – to make this step a failure." Ukrainian Defence Minister Rustem Umerov said the clashes were "small scale" and that the North Korean troops were not fighting as separate formations but were embedded in Russian units disguised as Buryats from the Russian Federation. On Saturday, Ukraine's military intelligence (GUR) had said Russia transferred more than 7,000 North Korean military personnel "to areas near Ukraine" in the last week of October – a much higher figure than the 3,000 North Korean soldiers South Korean and United States intelligence had said were in Russia's Kursk region on October 30.

Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, leveraging synthetic data generated via game engine-based simulations provides a promising and cost-effective solution to overcome this issue. Therefore, we present SynDroneVision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds, lighting conditions, and drone models, SynDroneVision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness, we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrate that SynDroneVision is a valuable resource for real-world data enrichment, achieving notable enhancements in model performance and robustness, while significantly reducing the time and costs of real-world data acquisition. SynDroneVision will be publicly released upon paper acceptance.