| 高伟亮,李宝鹏,彭海军,伍政华,郭维波.基于目标极化特性的机载电力线探测系统[J].电子测量与仪器学报,2026,40(1):228-237 |
| 基于目标极化特性的机载电力线探测系统 |
| Airborne power line detection system based on target polarization characteristics |
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| DOI： |
| 中文关键词: 低空飞行器 电力线防撞 探测系统 极化特性 L波段 |
| 英文关键词:low-altitude aircraft power line anti-collision detection system polarization characteristics L-band |
| 基金项目:国家电网科技项目（5211TZ8000V）资助 |
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| Author | Institution |
| Gao Weiliang | Qingdao Branch of Naval Aviation University, Qingdao 266041, China |
| Li Baopeng | Qingdao Branch of Naval Aviation University, Qingdao 266041, China |
| Peng Haijun | Qingdao Branch of Naval Aviation University, Qingdao 266041, China |
| Wu Zhenghua | The 38th Research Institute
of China Electronics Technology Group Corporation, Hefei 233088, China |
| Guo Weibo | Qingdao Branch of Naval Aviation University, Qingdao 266041, China |
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| 中文摘要: |
| 电力线目标因其细长物理尺寸、小的电磁散射面积，在低空飞行器防撞探测中较难发现，严重制约着低空飞行器的飞行安全。采用传统的光学雷达、毫米波雷达和激光雷达等对电力线进行探测，受能见度、发射功率、大气环境等因素制约，存在探测距离有限、识别概率低、虚警率高等缺陷。针对这一问题，设计了一种双极化技术体制机载电力线探测系统，利用电力线对L波段电磁波水平极化和垂直极化差异化散射特性，提取电力线目标回波双极化幅值、极化倾角作为极化特征向量，与传统多普勒检测状态向量一起构建目标检测增广状态向量。使用支持向量机（SVM）与卷积神经网络构建级联分类器，提取电力线目标的显性和隐形特性，采用2 000组电力线目标真实探测数据对分类器进行训练，以提高复杂地杂波中电力线目标的识别准确概率。经在田野环境和山区环境下的飞行试验表明，该电力线探测系统可在全天时、全天候工作，在1 500 m距离范围内，对电力线目标具有92%以上的正确识别概率，具有非常广的工程应用场景。 |
| 英文摘要: |
| Power line targets (high-voltage lines) are difficult to detect in low-altitude aircraft collision avoidance systems due to their slender physical dimensions and small electromagnetic scattering cross-section, which severely constrains flight safety. Conventional detection methods such as optical radar, millimeter-wave radar, and lidar are limited by factors like visibility, transmission power, and atmospheric conditions, resulting in restricted detection range, low recognition probability, and high false alarm rates. To address these issues, an airborne power line detection system based on a dual-polarization technical scheme is designed. This system utilizes the differential scattering characteristics of power lines under horizontal and vertical polarization in the L-band. It extracts the dual-polarization amplitude and polarization tilt angle from the power line echoes as polarization feature vectors, which are combined with traditional Doppler detection state vectors to form an augmented state vector for target detection. A cascaded classifier, employing support vector machines (SVM) and convolutional neural networks (CNN), is constructed to extract both explicit and implicit features of power line targets. The classifier is trained with 2 000 sets of real detection data to enhance the accurate recognition probability of power line targets in complex ground clutter. Flight tests conducted in field and mountainous environments demonstrate that the proposed power line detection system operates effectively all-weather and all-day, achieving a correct recognition probability of over 92% for power line targets within a range of 1 500 meters, which indicates broad potential for engineering applications |
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