基于涡旋雷达的飞鸟与旋翼无人机微动参数提取研究

周红平; 李睿; 李刘林; 郭忠义

doi:10.12000/JR25164

基于涡旋雷达的飞鸟与旋翼无人机微动参数提取研究

DOI: 10.12000/JR25164 CSTR: 32380.14.JR25164

合肥工业大学计算机与信息学院合肥 230009

基金项目: 国家自然科学基金(61775050)

详细信息

作者简介:
周红平，副研究员，硕士生导师，主要研究方向包括雷达干扰识别、复杂电磁环境下雷达目标识别、多目标追踪等

李　睿，硕士生，主要研究方向为涡旋雷达成像与涡旋雷达探测

李刘林，博士生，主要研究方向为声透镜、完美声涡旋以及涡旋通信等

郭忠义，教授，博士生导师，主要研究方向为涡旋雷达系统、智能传感系统、偏振智能信息处理、先进光通信技术、复杂电磁环境等

通讯作者:
郭忠义 guozhongyi@hfut.edu.cn

责任主编：罗迎 Corresponding Editor: LUO Ying

中图分类号: TN98
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出版历程
- 收稿日期: 2025-09-01

Micromotions Parameter Extraction of Birds and Rotary-wing unmanned aerial vehicles Based on Vortex Radar

School of Computer and Information, Hefei University of Technology, Hefei 230009, China

Funds: The National Natural Science Foundation of China (61775050)

More Information

Corresponding author: GUO Zhongyi, guozhongyi@hfut.edu.cn

摘要

摘要: 针对当前对飞鸟和旋翼无人机(UAVs)识别的迫切需求，该文提出了一种基于涡旋雷达的目标参数提取方法。工作核心聚焦于目标参数获取，在建模与参数提取维度上进行了系统扩展。首先分别对飞鸟主体运动与扑翼行为、以及旋翼无人机的旋翼转动与机体结构进行了数学建模与分析，推导了散射点对应的径向多普勒与旋转多普勒频移表达式，并从雷达回波信号中提取微多普勒特征，实现目标参数反演。对于鸟类目标，基于回波信号的频谱峰值提取径向多普勒估计飞行速度，并结合散射点旋转多普勒频移公式，通过短时傅里叶变换(STFT)计算旋转多普勒变化，实现对扑翼长度的估计，在低信噪比(SNR)条件下，扑翼长度估计误差保持在0.03 m以内。对于旋翼无人机目标，首先建立回波信号模型，推导微多普勒频移中径向与旋转分量的解析关系，并结合重构的多普勒信息与距离–时间维度，反演获得欧拉角、旋翼转速、旋翼长度以及机体到旋翼的距离等共6项结构与运动参数，各参数的估计误差显著低于传统基于单一多普勒的方法，其参数提取误差均保持在2%以内。仿真结果表明，该文提出的基于涡旋雷达的鸟类与旋翼无人机参数提取方法能够实现多参数的高精度获取，并在低信噪比条件下仍保持稳定可靠的性能，验证了方法的有效性与工程应用潜力。
- 涡旋电磁波 /
- 旋转多普勒频移 /
- 短时傅里叶变换 /
- 旋翼无人机 /
- 时频分析
Abstract: To address the urgent need to identify birds and rotary-wing unmanned aerial vehicles (UAVs), this paper proposes a vortex radar–based method for extracting micromotion parameters of targets. The study focused on target parameter acquisition and systematically extended target modeling and parameter extraction strategies. First, mathematical models were developed for the body motion and wing flapping behavior of birds as well as for the rotor movement characteristics and body structure of rotary-wing UAVs. Further, analytical expressions for the radial and rotational Doppler frequency shifts at scattering points were derived, and micro-Doppler features were extracted from radar echo signals to enable target parameter inversion. For bird targets, the radial Doppler frequency was estimated by extracting the spectral peak of the echo signal to obtain the flight velocity. In addition, by combining the rotational Doppler frequency shifts of the scattering points and analyzing the variations of the rotational Doppler frequency using the short-time Fourier transform (STFT), the wing-flapping length was estimated. Even under low signal-to-noise ratio (SNR) conditions, the estimation error of the wing-flapping length remained within 0.03 m. For rotary-wing UAV targets, an echo signal model was first constructed, and the analytical relationship between the radial and rotational components of the micro-Doppler frequency shift was derived. Using the reconstructed Doppler information and through range–time domain analysis, six structural and motion parameters were retrieved, including the Euler angles rotor rotational speed, rotor length, and the distance between the UAV body and rotor. The estimation errors for all parameters were significantly lower than those obtained with conventional approaches based on individual Doppler features, with all parameters remaining within 2%. Simulation results demonstrated that the proposed vortex radar–based parameter extraction method enables accurate multiparameter estimation for birds and rotary-wing UAVs. The method also exhibits stable and reliable performance under low SNR conditions, confirming its effectiveness and applicability in practical engineering scenarios.
- Vortex electromagnetic wave /
- Rotational Doppler frequency /
- Short-time Fourier transform /
- Rotary-wing UAVs /
- Time–frequency analysis

HTML全文

图 1 涡旋雷达探测原理图

Figure 1. Schematic diagram of vortex radar detection principle

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图 2 鸟类扑翼的运动学简化模型

Figure 2. Simplified kinematic model of bird wing flapping

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图 3 飞鸟运动的空间坐标系

Figure 3. Spatial coordinate system for bird motion

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图 4 雷达和目标的几何关系

Figure 4. Geometric relationship between radar and targe

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图 5 鸟类目标回波信号分析图

Figure 5. Analysis of the echo signal of the bird target

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图 6 鸟类目标时频图

Figure 6. Time–frequency representations of the bird target components

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图 7 不同OAM模式下的扑翼的微多普勒频率

Figure 7. Doppler frequencies of flapping wings with different OAM mode numbers

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图 8 旋翼无人机的回波信号分析图

Figure 8. Analysis of the echo signal of the rotor UAVs

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图 9 旋翼无人机时频曲线图

Figure 9. Time–frequency curves of the rotor UAVs

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图 10 旋翼无人机随机散射点的时频结果图

Figure 10. Time-Frequency results of random scatterers on rotor UAVs

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图 11 多目标场景下的多普勒频率

Figure 11. Doppler frequency characteristics in a multi-target scenario

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图 12 不同信噪比下参数估计的误差分析

Figure 12. Error analysis of parameter estimation under different SNRs

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图 13 多普勒频谱特征

Figure 13. Doppler spectrum characteristics

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表 1 主要仿真参数

Table 1. Primary simulation parameters

参数	数值
中心频率f₀	10 GHz
带宽	200 MHz
OAM模式大小	–10和10
鸟类目标的扑翼频率f_bird	2 Hz
鸟类目标的飞行速度v	2 m/s
鸟类目标的距离、方位角、俯仰角	(100 m, 60°, 10°)
鸟类目标的扑翼长度l_bird	0.5 m
旋翼无人机的旋转频率f	50 Hz
旋翼无人机的旋翼长度r_a	0.5 m
旋翼无人机机体到旋翼的长度l	1 m
旋翼无人机的欧拉角$ (\psi ,\theta ,\phi ) $	(60°, 30°, 45°)
旋翼无人机的距离、方位角、俯仰角	(500 m, 30°, 45°)

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表 2 估计结果以及归一化误差

Table 2. Estimated results and corresponding normalized errors

参数	真实值	估计值	δ
f_bird (Hz)	2	2	0%
v (m/s)	2	2.011	0.55%
l_bird (m)	0.5	0.485	3%
f (Hz)	50	50	0%
r_a (m)	0.5	0.5017	0.34%
l (m)	1	1.006	0.6%
ψ	60°	60.25°	0.41%
θ	30°	29.82°	0.6%
$ \phi $	45°	44.68°	0.7%

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表 3 飞鸟和旋翼无人机的参数及其特征对比表

Table 3. Comparison of parameters and characteristics between birds and rotary-wing UAVs

参数类型	具体参数	飞鸟目标	旋翼无人机目标
最大多普勒频率	f_max	243.36 Hz	8829.45 Hz
多普勒频谱图	/	非对称周期性调制	对称的正弦曲线簇
旋转多普勒频率值	f_D-a	0.632 Hz	5.735 Hz
运动频率	ω	4π	100π

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表 4 涡旋电磁波与传统平面波参数提取精度对比结果

Table 4. Comparison of parameter extraction accuracy between vortex electromagnetic waves and traditional plane waves

目标类型	参数名称	真实值	传统雷达估计值	传统雷达误差	涡旋雷达估计值	涡旋雷达误差
飞鸟[8]	f_bird (Hz)	2	2	0%	2	0%
	v (m/s)	10	9.9309	0.7%	9.9848	0.152%
	l_bird (m)	0.7	>0.63	<10%	0.682	1.058%
旋翼无人机[38]	f (Hz)	16.667	16.666	0.007%	16.667	0%
	r_a (m)	0.55	0.504	8.4%	0.5489	0.2%
	l (m)	1	/	/	1.006	0.6%
	(ψ, θ,$ \phi $)	(60 °,30 °,45 °)	/	/	(60.25 °,29.82 °,44.68 °)	/

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表 5 涡旋电磁波参数提取对比表(%)

Table 5. Comparison of parameter extraction methods using vortex electromagnetic waves(%)

方法	SNR(dB)	r_a	ω	ψ	θ	$ \phi $	l
文献[24]	5	18.00	2.50	17.10	/	/	/
	15	8.52	1.47	4.56	/	/	/
	25	5.60	1.06	1.33	/	/	/
文献[25]	5	17.98	1.86	12.47	5.56	6.01	/
	15	7.88	1.33	4.67	2.90	3.15	/
	25	5.48	0.96	2.88	0.56	1.95	/
本文	5	0.52	0.04	1.36	1.26	0.89	0.64
	15	0.42	0.01	0.82	0.92	0.65	0.49
	25	0.34	0	0.50	0.7	0.45	0.45

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