Israel activated a new aerial defense system – dubbed "Barak Magen" – for the first time on Sunday night, saying it intercepted and destroyed multiple Iranian drones. Israel activated a new aerial defense system – dubbed "Barak Magen," meaning "lightning shield" – for the first time on Sunday night, saying it intercepted and destroyed multiple Iranian drones. The Israeli Navy intercepted eight Iranian drones using the "Barak Magen" and its long-range air defense (LRAD) interceptor, which were launched from an Israeli navy Sa'ar 6 missile ship, the Israel Defense Forces (IDF) said in a statement. John Hannah, senior fellow at the National Security of America and the co-author of a report published earlier this month on Israel's defense against two massive Iranian missile attacks in 2024, told Fox News Digital on Monday that the air defense system "significantly enhances" the air and missile defense architecture of Israel's navy. "The Barak Magen is simply another arrow in the expanding quiver of Israel's highly sophisticated and increasingly diverse multi-tiered missile defense architecture – which was already, by leaps and bounds, the most advanced and experienced air defense system fielded by any country in the world," Hannah said.

The increase in vehicle numbers in California, driven by inadequate transportation systems and sparse speed cameras, necessitates effective vehicle speed detection. Detecting vehicle speeds per lane is critical for monitoring High-Occupancy Vehicle (HOV) lane speeds, distinguishing between cars and heavy vehicles with differing speed limits, and enforcing lane restrictions for heavy vehicles. While prior works utilized YOLO (You Only Look Once) for vehicle speed detection, they often lacked accuracy, failed to identify vehicle lanes, and offered limited or less practical classification categories. This study introduces a fine-tuned YOLOv11 model, trained on almost 800 bird's-eye view images, to enhance vehicle speed detection accuracy which is much higher compare to the previous works. The proposed system identifies the lane for each vehicle and classifies vehicles into two categories: cars and heavy vehicles. Designed to meet the specific requirements of traffic monitoring and regulation, the model also evaluates the effects of factors such as drone height, distance of Region of Interest (ROI), and vehicle speed on detection accuracy and speed measurement. Drone footage collected from Northern California was used to assess the proposed system. The fine-tuned YOLOv11 achieved its best performance with a mean absolute error (MAE) of 0.97 mph and mean squared error (MSE) of 0.94 $\text{mph}^2$, demonstrating its efficacy in addressing challenges in vehicle speed detection and classification.

Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety, reliability, and efficiency of these systems is paramount as they transition from research prototypes to mission-critical platforms. This paper presents a step-by-step guide to establishing a robust autonomous drone testing pipeline, covering each critical stage: Software-in-the-Loop (SIL) Simulation Testing, Hardware-in-the-Loop (HIL) Testing, Controlled Real-World Testing, and In-Field Testing. Using practical examples, including the marker-based autonomous landing system, we demonstrate how to systematically verify drone system behaviors, identify integration issues, and optimize performance. Furthermore, we highlight emerging trends shaping the future of drone testing, including the integration of Neurosymbolic and LLMs, creating co-simulation environments, and Digital Twin-enabled simulation-based testing techniques. By following this pipeline, developers and researchers can achieve comprehensive validation, minimize deployment risks, and prepare autonomous drones for safe and reliable real-world operations.

Israel and Iran have carried out a new wave of attacks on key cities, fuelling fears of an all-out sustained war, with heavy exchanges now entering a third day. Iranian missiles struck northern Israel, killing at least three people and wounding 13 others, late Saturday into Sunday, according to Israeli media. Israel targeted the Iranian defence ministry headquarters in Tehran early Sunday, according to the semi-official Tasnim news agency. Iranian officials also said the Shahran oil depot, northwest of Tehran, was struck by Israel. Tasnim News said operational and rescue forces arrived at the scene and are still working to extinguish the fire.

State Armor founder and CEO Michael Lucci on CCP-linked researchers residing at American universities, national security threats from China and the need to block the subversion with legislation. National security and China experts are warning that Israel's attack on Iran is an example of why Beijing's efforts to purchase land and other assets within the United States need to be stopped immediately. After the initial attacks began on Friday, news reports began surfacing indicating that Israel had secretly built a drone base on Iranian soil that it used to launch its attacks. The operation was years in the making, one Israeli security official told the Jewish Chronicle, adding that weapons systems and soldiers had been smuggled into the country ahead of time. "Look at the ways Israel penetrated Iran for sabotage operations. Now look at the Chinese companies and assets permeating the US power grid (solar converters), local law enforcement (DJI drones), and social media (TikTok)," China policy expert Michael Sobolik wrote in a post on X. "The CCP is preparing to paralyze us in a crisis."

The Mossad published footage of its operatives carrying out covert actions inside Iran prior to Israel's preemptive attack. Israel's overnight strike on Iran was not only one of the most ambitious aerial campaigns in recent history, it was the result of years of covert planning, surveillance and infiltration by Israeli intelligence. While dozens of fighter jets bombed nuclear and military targets across Iran early Friday morning, the groundwork had long been laid by Mossad agents working in lockstep with the Israeli military. Code-named "Am Kelavi" (Rising Lion), the preemptive operation was the product of unprecedented coordination between the Israeli air force, the Military Intelligence Directorate, Mossad and the country's defense industries. For years, they worked "shoulder to shoulder" to gather the intelligence files needed to eliminate Iran's most sensitive military and nuclear assets.

On Wednesday, United States Customs and Border Protection confirmed to 404 Media that it has been flying Predator drones over Los Angeles amid the LA protests. The military drones, a CBP statement said, "are supporting our federal law enforcement partners in the Greater Los Angeles area, including Immigration and Customs Enforcement, with aerial support of their operations." State-level law enforcement agencies across the US use various types of drones and other vehicles, like helicopters, to conduct aerial surveillance, and other agencies use drones in their operations as well. For example, the California Department of Forestry and Fire Protection "doubled its use of drones" this year, according to the office of Governor Gavin Newsom, as part of efforts to combat forest fires. However, CBP's MQ-9 Reaper drones, also known as Predator B drones, are military-caliber UAVs used for aerial reconnaissance that can be armed.

After Israel bombed a reported 100 locations in Iran, including residential buildings, Iran has responded by launching a drone swarm towards Israel. Reports indicate neighbouring countries, including Jordan, are shooting them down. Nour Odeh has this update from Amman.

--Vision-and-language navigation (VLN) is a long-standing challenge in autonomous robotics, aiming to empower agents with the ability to follow human instructions while navigating complex environments. Two key bottlenecks remain in this field: generalization to out-of-distribution environments and reliance on fixed discrete action spaces. To address these challenges, we propose Vision-Language Fly (VLFly), a framework tailored for Unmanned Aerial Vehicles (UAVs) to execute language-guided flight. Without the requirement for localization or active ranging sensors, VLFly outputs continuous velocity commands purely from egocentric observations captured by an onboard monocular camera. The VLFly integrates three modules: an instruction encoder based on a large language model (LLM) that reformulates high-level language into structured prompts, a goal retriever powered by a vision-language model (VLM) that matches these prompts to goal images via vision-language similarity, and a waypoint planner that generates executable trajectories for real-time UAV control. VLFly is evaluated across diverse simulation environments without additional fine-tuning and consistently outperforms all baselines. Moreover, real-world VLN tasks in indoor and outdoor environments under direct and indirect instructions demonstrate that VLFly achieves robust open-vocabulary goal understanding and generalized navigation capabilities, even in the presence of abstract language input. The capability of robots to execute complex tasks based on natural language instructions has long been a compelling objective in robotics and artificial intelligence (AI). Particularly within the field of autonomous navigation, this capability enables applications spanning home assistance [1], urban inspection [2], and environmental exploration [3]. Y uhang Zhang and Haosheng Y u are co-first authors.

The Maximum Entropy Reinforcement Learning (MaxEnt RL) framework is a leading approach for achieving efficient learning and robust performance across many RL tasks. However, MaxEnt methods have also been shown to struggle with performance-critical control problems in practice, where non-MaxEnt algorithms can successfully learn. In this work, we analyze how the trade-off between robustness and optimality affects the performance of MaxEnt algorithms in complex control tasks: while entropy maximization enhances exploration and robustness, it can also mislead policy optimization, leading to failure in tasks that require precise, low-entropy policies. Through experiments on a variety of control problems, we concretely demonstrate this misleading effect. Our analysis leads to better understanding of how to balance reward design and entropy maximization in challenging control problems.