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

Abstract

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

CLC Number:

V279
TN959

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.

Figures/Tables 17

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References 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

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Low-altitude target classification based on model conversion frequency estimation

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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