基于RMDLPP的雷达空中目标分类

doi:10.12305/j.issn.1001-506X.2024.04.10

Abstract

Abstract:

Aiming at the problem of exoticism of the intraclass dispersion matrix and sensitivity to isolated points in narrowband radar target data reduction of discriminant locality preserving projections (DLPP) in narrow-band radar target data, a narrow-band radar air targets classification method based on robust margin DLPP (RMDLPP) is proposed. Firstly, the Euclidean distance of the two sample points is correlated with the homogeneous sample mean value when calculating the distance between samples. Then, a certain number of boundary sample points are selected for processing and the DLPP objective function is optimized for dimensionality reduction. Finally, a high-performance classifier is used to distinguish the dimensionality reduction data and achieve the classification of aerial targets. Comparative experiments on X-band air-to-air alert radar measurements show that the proposed method has better classification accuracy and robustness to isolated points.

Key words: narrow-band radar, air targets classification, discriminant locality preserving projections (DLPP), maximum boundry criterion, dimensionality reduction

CLC Number:

TN957

Shuaikang LIU, Wei CAO, Zhiqiang GUAN, Xueling YANG, Jinxin XU. Classification of radar air targets based on RMDLPP[J]. Systems Engineering and Electronics, 2024, 46(4): 1220-1228.

Figures/Tables 14

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References 31

1	马林. 雷达目标识别技术综述[J]. 现代雷达, 2011, 33 (6): 1- 7. doi: 10.3969/j.issn.1004-7859.2011.06.001
	MA L . A review of radar target recognition technology[J]. Modern Radar, 2011, 33 (6): 1- 7. doi: 10.3969/j.issn.1004-7859.2011.06.001
2	WANG W T , TANG Z Y , CHEN Y C , et al. Aircraft target classification for conventional narrow-band radar with multi-wave gates sparse echo data[J]. Remote Sensing, 2019, 11 (22): 2700. doi: 10.3390/rs11222700
3	XIA S Q , ZHANG C W , CAI W Y , et al. Aircraft target classification method for conventional narrowband radar based on micro-Doppler effect[J]. Mathematical Problems in Engineering, 2022, 2022, 1- 11.
4	HE W K , SUN J , ZHANG X , et al. Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR[J]. Journal of Systems Engineering and Electronics, 2022, 33 (6): 1127- 1139.
5	ISWARIYA S , VALARMATHI J . Micro-Doppler signature based helicopter identification and classification through machine learning[J]. International Journal of Computers, 2021, 15, 23- 29. doi: 10.46300/9108.2021.15.4
6	ZHU L Z , ZHANG S N , CHEN S , et al. Classification of UAV-to-ground vehicles based on micro-Doppler effect and bispectrum analysis[J]. Signal, Image and Video Processing, 2019, 7, 22133- 22143.
7	SINGH A K , KIM Y H . Classification of drones using edge-enhanced micro-Doppler image based on CNN[J]. Traitement du Signal, 2021, 38 (4): 1033- 1039. doi: 10.18280/ts.380413
8	CHIPENGO U , SLIGAR A P , CANTA S M , et al. High fidelity physics simulation-based convolutional neural network for automotive radar target classification using micro-Doppler[J]. IEEE Access, 2021, 9, 82597- 82617. doi: 10.1109/ACCESS.2021.3085985
9	WANG W T , TANG Z Y , CHEN Y C , et al. Parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on micro-Doppler features using CNN[J]. Journal of Systems Engineering and Electronics, 2020, 31 (5): 884- 889. doi: 10.23919/JSEE.2020.000062
10	KIM J H , PARK D H , KIM H N . Performance analysis of deep-learning target classification algorithms using micro-Doppler images[J]. The Journal of Korean Institute of Communications and Information Sciences, 2021, 46 (3): 430- 439. doi: 10.7840/kics.2021.46.3.430
11	KANG H , KIM B K , PARK J S , et al. Drone elevation angle classification based on convolutional neural network with micro-Doppler of multipolarization[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 1- 5.
12	邵凯, 朱苗苗, 王光宇. 基于生成对抗与卷积神经网络的调制识别方法[J]. 系统工程与电子技术, 2022, 44 (3): 1036- 1043.
	SHAO K , ZHU M M , WANG G Y . Modulation identification method based on generative adversarial and convolutional neural networks[J]. Systems Engineering and Electronics, 2022, 44 (3): 1036- 1043.
13	刘凯, 张斌, 黄青华. 基于TCNN-BiLSTM网络的调制识别算法[J]. 系统工程与电子技术, 2020, 42 (8): 1841- 1849.
	LIU K , ZHANG B , HUANG Q H . Modulation recognition algorithm based on TCNN-BiLSTM network[J]. Systems Engineering and Electronics, 2020, 42 (8): 1841- 1849.
14	ROY D , SRIVASTAVA S , KUSUPATI A , et al. One size does not fit all: multi-scale, cascaded RNNs for radar classification[J]. ACM Trans.on Sensor Networks, 2021, 17 (2): 3501205.
15	WOLD S , ESBENSEN K , GELADI P , et al. Principal component analysis[J]. Chemometrics & Intelligent Laboratory Systems, 1987, 2 (1/3): 37- 52.
16	XU Y , YANG J Y , JIN Z . A novel method for Fisher discriminant analysis[J]. Pattern Recognition, 2004, 37 (2): 381- 384. doi: 10.1016/S0031-3203(03)00232-2
17	全大英, 唐泽雨, 陈赟, 等. 基于MSST及HOG特征提取的雷达辐射源信号识别[J]. 北京航空航天大学学报, 2022, 49 (3): 538- 547.
	QUAN D Y , TANG Z Y , CHEN Y , et al. Radar emitter signal recognition based on MSST and HOG feature extraction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 49 (3): 538- 547.
18	ZHU L Z , ZHANG S N , MA Q , et al. Classification of UAV-to-ground targets based on enhanced micro-Doppler features extracted via PCA and compressed sensing[J]. IEEE Sensors Journal, 2020, 20 (23): 14360- 14368. doi: 10.1109/JSEN.2020.3008439
19	HONG Y G , YANG Y J , PARK J . Linear discriminant analysis-based motion classification using distributed micro-Doppler radars with limited backhaul[J]. Sensors, 2021, 21 (9): 2924. doi: 10.3390/s21092924
20	BALASUBRAMANIAN M , TENENBAUM J B , SILVA V D , et al. The ISOMAP algorithm and topological stability[J]. Science, 2002, 295 (5552): 7. doi: 10.1126/science.295.5552.7a
21	ROWEIS S T , SAUL L K . Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290 (5500): 2323- 2326. doi: 10.1126/science.290.5500.2323
22	HE X , NIYOGI P . Locality preserving projections[J]. Advances in Neural Information Processing Systems, 2004, 16 (16): 153- 160.
23	HE X F , YAN S C , HU Y X , et al. Face recognition using Laplacianfaces[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2005, 27 (3): 328- 340. doi: 10.1109/TPAMI.2005.55
24	YU W , TENG X L , LIU C Q . Face recognition using discriminant locality preserving projections[J]. Image Vision Computing, 2006, 24 (3): 239- 248. doi: 10.1016/j.imavis.2005.11.006
25	HUANG S C , LU Z . Exponential discriminant locality preserving projection for face recognition[J]. Neurocomputing, 2016, 208 (5): 373- 377.
26	LU G F , WANG Y , ZOU J , et al. Matrix exponential based discriminant locality preserving projections for feature extraction[J]. Neural Networks, 2018, 97 (97): 127- 136.
27	HE Y L, LIANG L L, XU Y, et al. Novel discriminant locality preserving projections based on improved synthetic minority oversampling with application to fault diagnosis[C]//Proc. of the 10th Data Driven Control and Learning Systems Confe-rence, 2021: 463-467.
28	ZHONG F J , LI D F , ZHANG J S . Robust locality preserving projection based on maximum correntropy criterion[J]. Journal of Visual Communication & Image Representation, 2014, 25 (7): 1676- 1685.
29	RAN R S , QIN H , ZHANG S G , et al. Simple and robust locality preserving projections based on maximum difference criterion[J]. Neural Processing Letters, 2022, 54 (3): 1783- 1804. doi: 10.1007/s11063-021-10706-4
30	韦佳, 彭宏, 林毅申. 基于改进距离的孤立点检测方法[J]. 华南理工大学学报(自然科学版), 2008, 36 (9): 25- 30.
	WEI J , PENG H , LIN Y S . Outlier detection method based on improved distance[J]. Journal of South China University of Technology (Natural Science Edition), 2008, 36 (9): 25- 30.
31	张辉, 刘万军, 吕欢欢. 小波核局部Fisher判别分析的高光谱遥感影像特征提取[J]. 模式识别与人工智能, 2019, 32 (7): 624- 632.
	ZHANG H , LIU W J , LYU H H . Feature extraction of hyperspectral remote sensing images for wavelet nucleus local Fisher discriminant analysis[J]. Pattern Recognition and Artificial Intelligence, 2019, 32 (7): 624- 632.

[1]	Yichang CHEN, Xin XIONG, Wantian WANG. Target sortie identification method of narrow-band radar based on sparse fractional Fourier transform [J]. Systems Engineering and Electronics, 2021, 43(8): 2129-2136.
[2]	HU Xiao-wei, TONG Ning-ning, HE Xing-yu, LEI Teng, WANG Yu-chen. High-speed spinning target imaging based on narrow-band radar networks [J]. Systems Engineering and Electronics, 2016, 38(4): 792-798.
[3]	LIANG Sheng-jie, ZHANG Zhi-hua, CUI Li-lin, ZHONG Qiang-hui. Dimensionality reduction method based on PCA and KICA [J]. Journal of Systems Engineering and Electronics, 2011, 33(9): 2144-2148.
[4]	LIU Jian-jun, XIA Sheng-ping, YU Wen-xian. LLE algorithm based on RSOM clustering [J]. Journal of Systems Engineering and Electronics, 2009, 31(2): 468-470.

Classification of radar air targets based on RMDLPP

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