引文格式：同非,苟雨晴,张坤,等.探鸟雷达系统设计、信号处理与分类识别方法综述[J].西安邮电大学学报,2026,31(5):1-17.
TONG Fei, GOU Yuqing, ZHANG Kun, et al. A review of bird radar system design, signal processing, classification and recognition methods[J]. Journal of Xi'an University of Posts and Telecommunications, 2026,31(5):1-17
探鸟雷达系统设计、信号处理与分类识别方法综述
同 非1，苟雨晴2，张 坤2，许京伟1，李 琳2，姜可亭2，廖桂生1
（1.西安电子科技大学 雷达信号处理全国重点实验室，陕西 西安 710071；2.西北工业大学 自动化学院，陕西 西安 710129）
摘要：近年来，生态环境的持续改善促使低空鸟类种类与规模显著增加，但由此引发的航空器鸟击、航班起降干扰，以及低空管控场景中无人机的虚警等事件频发，给低空空域安全带来严重威胁。在复杂背景下实现飞鸟的有效探测与飞鸟/无人机的精准区分，已成为当前低空安防领域亟待解决的技术问题。而探鸟雷达凭借全天候、大范围空域、高数据率等优势，成为低慢小目标感知的核心装备。针对飞鸟与无人机这两类典型易混淆目标，系统梳理了探鸟雷达在目标回波数学模型、信号处理、分类识别算法及数据集建设等方面的研究脉络，重点剖析了微多普勒特征与极化特征的核心机理，深入探讨了应用于低慢小目标识别的深度学习算法以及多模态融合机制在探鸟雷达系统研制与算法迭代中的落地方案。分析表明，搭载轻量化端到端神经网络、多模态数据融合驱动、多站组网部署正成为探鸟雷达的未来发展趋势，但高质量标注数据集依然匮乏。综述厘清了探鸟雷达核心技术的演进路径与现存局限，旨在为探鸟雷达系统的研制迭代和飞鸟/无人机目标分类识别技术的持续深入研究提供技术参考与启示。
关键词：探鸟雷达；目标建模；信号处理；目标识别；深度学习
中图分类号：TN957.52；TN957.51 文献标志码：A
文章编号：2095-6533(2026)05-0001-17
A review of bird radar system design, signal processing, classification and recognition methods
TONG Fei¹, GOU Yuqing², ZHANG Kun², XU Jingwei¹, LI Lin²,
JIANG Keting², LIAO Guisheng¹
(1.National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China; 2.School of Automation, Northwestern Polytechnical University, Xi’an 710129, China)
Abstract: In recent years, the continuous improvement of the ecological environment has led to a significant increase in the types and numbers of low-altitude birds. However, this has also caused frequent incidents such as bird strikes on aircrafts, disruptions to flight takeoffs and landings, and false alarms of drones in low-altitude management scenarios, posing serious threats to the safety of low-altitude airspace. Achieving effective detection of birds and precise differentiation between birds and drones in complex environments has become a pressing technical issue in the field of low-altitude security. Bird-detecting radar, with advantages such as all-weather capability, wide-area airspace coverage, and high data rates, has become a core device for detecting low, slow, and small targets. Focusing on birds and drones as two typical easily-confused targets, researches on bird-detecting radar in areas such as target echo mathematical modeling, signal processing, feature extraction, classification algorithms, and dataset construction are systematically summarized. It focuses on analyzing the core mechanisms of micro-Doppler features and polarization features, and explores in-depth the implementation of deep learning algorithms and multimodal fusion mechanisms for low, slow, and small target identification in the development and algorithm iteration of bird-detecting radar systems. Analysis shows that equipping bird-detecting radar with lightweight end-to-end neural networks, driven by multimodal data fusion, and deployed in multi-station networks is becoming the future development trend of bird detection radars, but high-quality annotated datasets are still lacking. The evolution path and existing limitations of core bird-detecting radar technologies are clarified, aiming to provide technical reference and insights for the development and iteration of bird-detecting radar systems, as well as the continued in-depth research of bird and drone target classification and recognition technologies.
Keywords: bird detection radar; target modeling; signal processing; target recognition; deep learning
基金项目：装备发展部十四五应用创新预研项目(627010103)
作者简介：同非（1987—），男，陕西渭南人，西安电子科技大学博士研究生，主要研究方向为雷达系统设计、雷达信号处理和雷达目标分类识别。E-mail：shadytong@126.com
苟雨晴（2002—），女，河北保定人，西北工业大学硕士研究生，主要研究方向为雷达信号处理、稀疏阵列设计。E-mail：gouyuqing@mail.nwpu.edu.cn
张坤（2001—），男，山西阳泉人，西北工业大学硕士研究生，主要研究方向为雷达目标分类识别。E-mail：zhangkun123@mail.nwpu.edu.cn
许京伟（1987—），男，山东日照人，西安电子科技大学教授、博士生导师，主要研究方向为雷达信号处理和雷达系统设计。E-mail：jwxu@xidian.edu.cn
李琳（1986—），女，山西运城人，博士，西北工业大学副教授、博士生导师，主要研究方向为智能优化、雷达目标识别等。E-mail：linli@nwpu.edu.cn
姜可亭（2005—），女，江西上饶人，西北工业大学本科生，主要研究方向为雷达目标分类识别。E-mail：3388374331@qq.com
廖桂生（1963—），男，广西桂林人，西安电子科技大学教授，雷达信号处理全国重点实验室副主任，主要研究方向为阵列信号处理、机载预警雷达和分布式卫星雷达信号处理。E-mail：Gsliao@xidian.edu.cn