中国军方科学家称，他们开发出一种算法，可利用民航飞行数据帮助识别电子战单位。
Chinese military scientists claim they have developed an algorithm that could help identify electronic warfare units using civil aviation flight data.
研究人员表示，他们在与伊朗冲突期间，发现了两个伪装位置的干扰源，位于以色列附近。团队未解释为何将焦点放在以色列军方。
The researchers said they had located two sources of interference that were spoofing their locations near Israel during the conflict with Iran. The team did not explain why they had focused on the Israeli military.
研究人员估计，这两个单位位于海法附近，定位误差在10.9公里至18.9公里（6.7至11.7英里）之间。《南华早报》无法独立核实这些说法。
The researchers estimated the locations of the two units as being near Haifa, with reported positioning errors between 10.9km and 18.9km (6.7-11.7 miles). The South China Morning Post could not independently verify the claims.
该研究由郑州解放军信息工程大学杜兰及其团队进行，于10月7日在同行评审期刊《测绘科学与技术杂志》在线发表。
The study, conducted by Du Lan and a team at the People’s Liberation Army’s Information Engineering University in Zhengzhou, was published online on October 7 in the peer-reviewed Journal of Geomatics Science and Technology.
研究人员分析了来自开源民航系统的43天航空器追踪数据，覆盖从2月27日（美以对伊朗发动袭击前一天）至4月10日的时期。
The researchers analysed 43 days of aircraft-tracking data from open-source civil aviation systems, covering the period from February 27, the day before the US and Israel launched their attack on Iran, to April 10.
电子干扰可能导致航空数据出现跳至远处位置的突变，或与正常航迹不符的移动。团队的算法利用异常航迹来估算干扰源的可能位置。研究人员写道：“该方法无需额外监控设备，成本低且及时。”
Electronic interference may result in aviation data that shows abrupt jumps to a distant location or movements inconsistent with a normal flight path. The team’s algorithm used abnormal flight paths to estimate the possible locations of the sources of this interference. “The method requires no additional monitoring equipment and is low-cost and timely,” the researchers wrote.
“它为民航安全提供关键技术支持，并提升海外冲突地区对欺骗性干扰的态势感知。”团队在地中海识别出两个不同区域。“目标区1位于以色列北部主要城市海法西北约80公里处，目标区2位于加沙地带西北约30公里处。两个目标区均位于地中海。”作者写道。
“It provides key technical support for civil aviation safety and situational awareness of spoofing interference in overseas conflict zones.”The team identified two distinct areas in the Mediterranean. “Target area 1 was about 80km northwest of Haifa, a major city in northern Israel, while target area 2 was about 30km northwest of the Gaza Strip. Both target areas were in the Mediterranean Sea,” the authors wrote.
研究人员指出，不同的模式可能反映了针对不同类型无人机威胁的应对尝试，尽管预定目标无法直接验证。
The researchers suggested that the different patterns could reflect attempts to counter different types of drone threats, although the intended targets could not be directly verified.
杜及其团队写道：“对应目标区域1的欺骗源被推断为主要针对具有某种抗干扰能力的远程自杀式无人机，例如伊朗的Shahed-136，这需要更复杂的动态欺骗策略。”
“The spoofing source corresponding to target area 1 is inferred to have mainly countered long-range suicide drones with some resistance to spoofing, such as Iran’s Shahed-136, requiring a more complex dynamic spoofing strategy,” Du and her team wrote.
“目标区域2被推断为主要针对加沙地带和胡塞武装（也门）所使用的低成本、短程无人机，这些无人机对导航欺骗的抵抗力较弱，可通过静态单点欺骗策略进行应对。”杜及其团队使用了来自自动相关监视广播（ADS-B）系统的公开飞机位置和速度数据。
“Target area 2 is inferred to have mainly countered low-cost, short-range drones used by groups in Gaza and the Houthi movement [from Yemen], whose resistance to navigation spoofing is weaker and which can be countered with a static, single-point spoofing strategy.”Du and her team used publicly available aircraft position and speed data from the Automatic Dependent Surveillance-Broadcast system.
研究人员最初利用速度阈值筛选异常轨迹，这些阈值可能表明飞行数据正被欺骗。
The researchers initially screened abnormal tracks using speed thresholds that were possible indicators that flight data was being spoofed.
随后，他们加入了额外的时间、地点、高度和运行时段限制，以过滤掉数据误差、飞机机动、低空飞行以及干扰源开启和关闭等因素。
They then applied additional time, location, altitude and operating-period constraints to filter out factors such as data errors, aircraft manoeuvres, low-altitude flights and sources of interference being switched on and off.
该团队表示，未来工作将结合船舶的导航数据，将该方法扩展至民用航空之外。
The team said future work would incorporate navigation data from ships to extend the method beyond civil aviation.