基于模型转换频率估计的低空目标分类

doi:10.12305/j.issn.1001-506X.2021.04.09

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (4): 927-936.doi: 10.12305/j.issn.1001-506X.2021.04.09

基于模型转换频率估计的低空目标分类

陈唯实^1,*(), 黄毅峰¹(), 陈小龙²(), 卢贤锋¹(), 张洁¹()

1. 中国民航科学技术研究院, 北京 100028
2. 海军航空大学, 山东烟台 264001

收稿日期:2020-04-14 出版日期:2021-03-25 发布日期:2021-03-31
通讯作者: 陈唯实 E-mail:chenwsh@mail.castc.org.cn;huangyf@mail.castc.org.cn;cxlcxl1209@163.com;luxf@mail.castc.org.cn;zhangjie@mail.castc.org.cn
作者简介:陈唯实 (1982-), 男, 研究员, 博士, 主要研究方向为无人机反制、雷达目标检测与跟踪、机场安全运行技术。E-mail: chenwsh@mail.castc.org.cn|黄毅峰 (1964-), 男, 工程师，本科, 主要研究方向为飞行安全管理、机场安全运行技术。E-mail: huangyf@mail.castc.org.cn|陈小龙 (1985-), 男, 副教授, 博士, 主要研究方向为智能雷达信号处理、动目标检测。E-mail: cxlcxl1209@163.com|卢贤锋 (1978-), 男, 高级工程师, 硕士, 主要研究方向为机场安全运行技术。E-mail: luxf@mail.castc.org.cn|张洁 (1981-), 女, 高级工程师, 硕士, 主要研究方向为机场鸟击防范技术。E-mail: zhangjie@mail.castc.org.cn
基金资助:
国家自然科学基金民航联合基金(U1933135);国家重点研发计划(2016YFC0800406)

Low-altitude target classification based on model conversion frequency estimation

Weishi CHEN^1,*(), Yifeng HUANG¹(), Xiaolong CHEN²(), Xianfeng LU¹(), Jie ZHANG¹()

1. China Academy of Civil Aviation Science and Technology, Beijing 100028, China
2. Naval Aviation University, Yantai 264001, China

Received:2020-04-14 Online:2021-03-25 Published:2021-03-31
Contact: Weishi CHEN E-mail:chenwsh@mail.castc.org.cn;huangyf@mail.castc.org.cn;cxlcxl1209@163.com;luxf@mail.castc.org.cn;zhangjie@mail.castc.org.cn

摘要/Abstract

摘要：

为解决传统机械扫描预警雷达的低空目标分类问题, 提出一种利用模型转换频率估计的飞鸟与无人机目标分类方法。首先, 建立可能的无人机和飞鸟目标运动模型, 进而对目标轨迹进行多模型滤波和平滑处理。然后, 通过估计目标的模型转换频率实现目标分类。仿真结果验证了所提方法的鲁棒性和有效性, 证明经过平滑处理的目标分类结果优于仅经过滤波处理。针对低空预警雷达在机场和海岸环境中采集的真实数据, 在无人机频繁改变飞行方向模拟飞鸟机动性特征的情况下, 所提方法仍然可以较好地跟踪和区分无人机与飞鸟目标, 大幅降低了雷达系统的虚警率。

关键词: 无人机, 目标分类, 目标跟踪, 多模型滤波, 模型转换, 预警雷达

Abstract:

In order to solve the problem of low altitude target classification of traditional mechanical scanning early warning radar, a method for target classification of flying bird and unmanned aerial vehicle (UAV) based on model conversion frequency estimation is proposed. Firstly, the possible UAV and flying bird target motion models are established, the multi-model filtering and smoothing are applied to the target trajectory. And then the target classification is realized by the model conversion frequency of the target estimation. Simulation results verify the robustness and effectiveness of the proposed method, and prove that the target classification result after smoothing is better than that only after filtering. According to the real data collected by low altitude early warning radar in the airport and coastal environment, the proposed method can still track and distinguish UAV and bird targets, and greatly reduce the false alarm rate of the radar system when UAV frequently changes its flight direction to simulate the mobility characteristics of flying bird.

Key words: unmanned aerial vehicle (UAV), target classification, target tracking, multi-model filtering, model conversion, early warning radar

中图分类号:

V279
TN959

陈唯实, 黄毅峰, 陈小龙, 卢贤锋, 张洁. 基于模型转换频率估计的低空目标分类[J]. 系统工程与电子技术, 2021, 43(4): 927-936.

Weishi CHEN, Yifeng HUANG, Xiaolong CHEN, Xianfeng LU, Jie ZHANG. Low-altitude target classification based on model conversion frequency estimation[J]. Systems Engineering and Electronics, 2021, 43(4): 927-936.

图/表 17

图1

图2

图3

表1

图4

图5

图6

图7

图8

图9

表2

图10

表3

图11

图12

图13

表4

参考文献 29

1	金伟, 尚勇. 中国无人机安全监管[J]. 科技导报, 2019, 37 (14): 66- 77.
	JIN W , SHANG Y . The safety supervision of unmanned aircraft systems in China[J]. Science & Technology Review, 2019, 37 (14): 66- 77.
2	SAMARAS S , DIAMANTIDOU E , ATALOGLOU D , et al. Deep learning on multi sensor data for counter UAV applications-a systematic review[J]. Sensors, 2019, 19 (22): 4837- 4871. doi: 10.3390/s19224837
3	ZHANG M , DIAO M , GUO L . Convolutional neural networks for automatic cognitive radio waveform recognition[J]. IEEE Access, 2017, 5, 11074- 11082. doi: 10.1109/ACCESS.2017.2716191
4	CRAYE C, ARDJOUNE S. Spatio-temporal semantic segmentation for drone detection[C]//Proc. of the IEEE 16th International Conference on Advanced Video and Signal based Surveillance, 2019.
5	KIM J , KIM D . Neural network based real-time UAV detection and analysis by sound[J]. Journal of Advanced Information Technology and Convergence, 2018, 8 (1): 43- 52. doi: 10.14801/JAITC.2018.8.1.43
6	陈小龙, 关键, 黄勇, 等. 雷达低可观测目标探测技术[J]. 科技导报, 2017, 35 (11): 30- 38.
	CHEN X L , GUAN J , HUANG Y , et al. Radar low-observable target detection[J]. Science & Technology Review, 2017, 35 (11): 30- 38.
7	SAQIB M, KHAN S D, SHARMA N, et al. A study on detecting drones using deep convolutional neural networks[C]//Proc. of the IEEE 14th International Conference on Advanced Video and Signal based Surveillance, 2017.
8	VASILEIOS M D A, ANASTASIOS D D Z, DARAS P. Does deep super-resolution enhance UAV detection[C]//Proc. of the IEEE 16th International Conference on Advanced Video and Signal Based Surveillance, 2019.
9	AKER C, KALKAN S. Using deep networks for drone detection[C]//Proc. of the IEEE 14th International Conference on Advanced Video and Signal Based Surveillance, 2017.
10	ROZANTSEV A , LEPETIT V , FUA P . Detecting flying objects using a single moving camera[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2016, 39 (5): 879- 892.
11	WILKINSON B , ELLISON C , NYKAZA E T , et al. Deep learning for unsupervised separation of environmental noise sources[J]. Journal of the Acoustical Society of America, 2017, 141 (5): 3964- 3964.
12	PARK S, SHIN S, KIM Y, et al. Combination of radar and audio sensors for identification of rotor-type unmanned aerial vehicles (UAVs)[C]//Proc. of the IEEE Sensors, 2015.
13	LIU H, WEI Z Q, CHEN Y T, et al. Drone detection based on an audio-assisted camera array[C] //Proc. of the IEEE 3rd International Conference on Multimedia Big Data, 2017: 402-406.
14	KIM J, PARK C, AHN J, et al. Real-time UAV sound detection and analysis system[C]//Proc. of the IEEE Sensors Applications Symposium, 2017.
15	CHEN V C . The micro-Doppler effect in radar[M]. Norwood: Artech House, 2011.
16	WANG L , TANG J , LIAO Q M . A study on radar target detection based on deep neural networks[J]. IEEE Sensors Letters, 2019, 3 (3): 7000504.
17	HARMANNY R, DEWIT J, CABIC G P. Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram[C]//Proc. of the 11th European Radar Conference, 2014: 165-168.
18	MOLCHANOV P , HARMANNY R I , DEWIT J , et al. Classification of small UAVs and birds by micro-Do ppler signatures[J]. International Journal of Microwave and Wireless Technologies, 2013, 6 (3/4): 435- 444.
19	FUHRMANN L, BIALLAWONS O, KLARE J, et al. Micro-Doppler analysis and classification of UAVs at Ka band[C]//Proc. of the 18th International Radar Symposium, 2017.
20	REN J F , JIANG X D . Regularized 2D complex-log spectral analysis and subspace reliability analysis of micro-Doppler signature for UAV detection[J]. Pattern Recognition, 2017, 69 (C): 225- 237.
21	OH B S , GUO X , WAN F Y , et al. Micro-Doppler mini-UAV classification using empirical-mode decomposition features[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 15 (2): 227- 231.
22	MA X, OH B S, SUN L, et al. EMD-based entropy features for micro-Doppler mini-UAV classification[C]//Proc. of the 24th International Conference on Pattern Recognition, 2018: 1295-1300.
23	刘玉琪, 易建新, 万显荣, 等. 数字电视外辐射源雷达多旋翼无人机微多普勒效应实验研究[J]. 雷达学报, 2018, 7 (5): 585- 592.
	LIU Y Q , YI J X , WAN X R , et al. Experimental research on micro-Doppler effect of multi-rotor drone with digital television based passive radar[J]. Journal of Radars, 2018, 7 (5): 585- 592.
24	PATEL J S , FIORANELLI F , ANDERSON D . Review of radar classification and RCS characterization techniques for small UAVs or drones[J]. IET Radar Sonar & Navigation, 2018, 12 (9): 911- 919.
25	TORVIK B , OLSEN K E , GRIFFITHS H . Classification of birds and UAVs based on radar polarimetry[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13 (9): 1305- 1309. doi: 10.1109/LGRS.2016.2582538
26	MESSINA M, PINELLI G. Classification of drones with a surveillance radar signal[C]//Proc. of the 12th International Conference on Computer Vision Systems, 2019.
27	陈唯实, 刘佳, 陈小龙, 等. 基于运动模型的低空非合作无人机目标识别[J]. 北京航空航天大学学报, 2019, 45 (4): 687- 694.
	CHEN W S , LIU J , CHEN X L , et al. Non-cooperative UAV target recognition in low-altitude airspace based on motion model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (4): 687- 694.
28	LI X R , BAR-SHALOM Y . Design of an interacting multiple model algorithm for air traffic control tracking[J]. IEEE Trans.on Control Systems Technology, 1993, 1 (3): 186- 194. doi: 10.1109/87.251886
29	陈唯实. 基于时域特性的非相参雷达目标检测与跟踪[J]. 系统工程与电子技术, 2016, 38 (8): 1800- 1807.
	CHEN W S . Incoherent radar target detection and tracking with temporal features[J]. Systems Engineering and Electronics, 2016, 38 (8): 1800- 1807.

N_T	目标	RMSE
N_T	目标	滤波	平滑
50	无人机	0.164 5	0.092 2
50	飞鸟	0.214 3	0.119 8
100	无人机	0.159 5	0.083 1
100	飞鸟	0.218 8	0.117 5
150	无人机	0.156 7	0.080 3
150	飞鸟	0.221 0	0.116 0
200	无人机	0.155 6	0.078 9
200	飞鸟	0.222 0	0.115 8

N_T	滤波/s	(滤波+平滑)/s	平滑增加耗时/s
30	0.009 2	0.033 4	0.024 2
40	0.011 4	0.042 2	0.030 8
50	0.014 4	0.054 1	0.039 7

[1]	孙田野, 孙伟, 吴建军. 改进Quatre算法的无人机编队快速集结方法[J]. 系统工程与电子技术, 2022, 44(9): 2840-2848.
[2]	余婧, 雍恩米, 陈汉洋, 郝东, 张显才. 面向多无人机协同对地攻击的双层任务规划方法[J]. 系统工程与电子技术, 2022, 44(9): 2849-2857.
[3]	徐星光, 王晓峰, 姚璐, 任章. 固定翼无人机编队构型与通信拓扑优化[J]. 系统工程与电子技术, 2022, 44(9): 2936-2946.
[4]	仇祝令, 查宇飞, 李振宇, 李禹铭, 张鹏, 朱川. 基于多模型蒸馏的时间正则化相关滤波跟踪算法[J]. 系统工程与电子技术, 2022, 44(8): 2448-2456.
[5]	侯子林, 程婷, 彭瀚. 基于量测转换序贯滤波的GMPHD机动目标跟踪[J]. 系统工程与电子技术, 2022, 44(8): 2474-2482.
[6]	杨建峰, 肖和业, 李亮, 白俊强, 董维浩. 基于模糊聚类和专家评分机制的无人机多层次模块划分方法[J]. 系统工程与电子技术, 2022, 44(8): 2530-2539.
[7]	史浩然, 卢发兴, 祁江鑫, 杨光. 基于辅助信标的无人机协同目标跟踪[J]. 系统工程与电子技术, 2022, 44(7): 2302-2310.
[8]	金国栋, 薛远亮, 谭力宁, 许剑锟. 基于孪生神经网络的目标跟踪算法进展研究[J]. 系统工程与电子技术, 2022, 44(6): 1805-1822.
[9]	翟光, 王妍欣, 孙一勇. 基于低轨星网的多目标协同跟踪滤波技术[J]. 系统工程与电子技术, 2022, 44(6): 1957-1967.
[10]	王帅, 向建军, 彭芳, 唐书娟. 基于新最速下降法的目标跟踪算法[J]. 系统工程与电子技术, 2022, 44(5): 1512-1519.
[11]	辛怀声, 曹晨. 基于交互多模型的分组δ-广义标签多伯努利算法[J]. 系统工程与电子技术, 2022, 44(4): 1128-1138.
[12]	卢元杰, 刘志敏, 孙智孝, 阚东. 基于模型的无人机系统架构综合评估方法[J]. 系统工程与电子技术, 2022, 44(4): 1239-1245.
[13]	李园, 史宪铭, 李亚娟, 赵美. 基于Adaboost的作战目标属性判定方法[J]. 系统工程与电子技术, 2022, 44(4): 1256-1262.
[14]	李洪瑶, 李小强, 韩心中, 谢学立, 席建祥. 基于决策融合的多无人机协同目标检测识别算法[J]. 系统工程与电子技术, 2022, 44(3): 746-754.
[15]	谢家豪, 黄树彩, 韦道知, 张曌宇, 王文豪. 基于P_EV准则的不确定混合多传感器联盟求解[J]. 系统工程与电子技术, 2022, 44(3): 819-826.

基于模型转换频率估计的低空目标分类

Low-altitude target classification based on model conversion frequency estimation

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 29

相关文章 15

编辑推荐

Metrics

本文评价

S	滤波		平滑
S	无人机	飞鸟	无人机	飞鸟
0.000 1	1	20	1	20
0.001	3	18	2	19
0.01	7	14	5	16
0.1	9	12	8	13
0.12	9	12	8	13
0.14	14	7	12	9
0.16	16	5	15	6
0.18	17	4	17	4

S	滤波		平滑
S	无人机	飞鸟	无人机	飞鸟
0.04	1	20	1	20
0.06	3	18	1	20
0.08	4	17	3	18
0.10	6	15	5	16
0.12	8	13	6	15
0.14	15	6	12	9
0.16	18	3	17	4