This article is reproduced from: Communication radar electronic warfare, author: Tang Chenliang Unmanned Aerial Vehicles (UAVs), like the Predator, are well-suited for various surveillance tasks due to their ability to provide real-time video that operators can easily interpret. However, one major limitation is the narrow field of view of the onboard cameras, which restricts the area they can monitor at any given time. As a result, the Predator platform alone isn't ideal for large-area search and surveillance. This is where Moving Target Indication Radar (MTI) comes into play. MTI systems can detect moving objects over a wide area, making them excellent for initial detection. But while MTI provides useful data for automated decision-making, it lacks the detail needed to confirm specific activities. Therefore, while the onboard MTI system can identify activity in large areas, it cannot confirm what exactly is happening. Researchers explored how combining these two sensing technologies could improve situational awareness. The idea was to use the MTI algorithm to alert the Predator drone’s camera operator when suspicious activity was detected. This approach aimed to enhance the operator’s ability to locate and analyze potential threats more efficiently. Although the algorithm itself doesn’t make decisions, it can guide the operator by providing targeted cues. However, a semi-automatic system may not always lead to better human performance. To ensure effectiveness, the system must deliver clear and actionable information to the operator. To achieve this, an integrated lab was developed, featuring real-time simulation, operator-in-the-loop experiments, and distributed data collection. These tools helped test how well the combination of MTI and UAV video could support military operations. The study focused on counter-insurgency scenarios, where continuous monitoring of large areas is crucial. MTI is effective for detecting movement, but identifying specific threats requires a more detailed sensor—like the Predator’s video feed. While the video provides clarity, its limited field of view makes it unsuitable for wide-area coverage alone. To bridge this gap, researchers aimed to integrate the strengths of both systems. By using MTI to detect suspicious activity and then directing the drone's camera toward those areas, the system could improve overall efficiency. The key component in this integration is the cueing algorithm. It analyzes MTI data to detect signs of activity, such as a fleet, and then alerts the operator to focus the drone’s camera on the relevant area. This process was tested using data from the Lincoln Multitasking ISR Experimental Platform (LiMIT), specifically during the “silent hammer experiment.†The goal was to enable the Predator drone to effectively survey large areas with its own recognition capabilities. The experiment compared how well a guided drone performed versus a non-guided one, measuring improvements in detection accuracy and response time. In building the testing environment, the first step was simulating ground vehicles, including fleets and riot vehicles. Next, a Moving Target Indication Radar (MTI) system was simulated to detect and track moving targets across a wide area. The MTI data was then processed by the cueing algorithm, which automatically analyzed the signals and highlighted areas of interest. For instance, if the system was looking for a fleet, it would indicate the presence of suspicious activity, allowing the operator to investigate further using the drone’s camera. huasheng Siren Alarm,huasheng Siren Alarm for car,huasheng professional Siren Alarm Gaoyou Huasheng Electronics Co., Ltd. , https://www.yzelechs.com