基于流形變換的信息幾何雷達目標檢測方法

楊政; 程永強; 吳昊; 楊陽; 黎湘; 王宏強

doi:10.11999/JEIT240286

基于流形變換的信息幾何雷達目標檢測方法

doi: 10.11999/JEIT240286

國防科技大學電子科學學院長沙 410073

基金項目: 國家自然科學基金(61921001)，湖南省杰出青年基金(2022JJ10063)

詳細信息

作者簡介:
楊政：男，博士生，研究方向為雷達目標檢測與信息幾何

程永強：男，教授，研究方向為雷達目標檢測、信息幾何和雷達前視成像

吳昊：男，講師，研究方向為雷達目標檢測與信息幾何

楊陽：男，博士生，研究方向為雷達前視成像

黎湘：男，教授，研究方向為目標識別、信號檢測和雷達成像

王宏強：男，教授，研究方向為太赫茲技術、量子雷達和雷達目標特性

通訊作者:
程永強　nudtyqcheng@gmail.com

中圖分類號: TN957.51
計量
- 文章訪問數(shù): 248
- HTML全文瀏覽量: 88
- PDF下載量: 69
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-04-16
- 修回日期: 2024-09-06
- 網(wǎng)絡出版日期: 2024-09-28
- 刊出日期: 2024-11-10

Manifold Transformation-based Information Geometry Radar Target Detection Method

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (61921001), The Distinguished Youth Science Foundation of Hunan Province (2022JJ10063)

摘要

摘要: 基于信息幾何的目標檢測方法為解決雷達目標檢測問題提供了新的技術途徑。該文以矩陣信息幾何理論為基礎，考慮復雜非均勻環(huán)境下，回波信雜比低，目標與雜波在矩陣流形上區(qū)分性差，導致傳統(tǒng)信息幾何檢測器性能受限的問題，提出一種基于流形變換的信息幾何檢測器。具體地，該文建立了流形到流形映射變換，并提出待檢測單元與雜波中心的幾何距離聯(lián)合優(yōu)化方法，從而增強變換后流形上目標與雜波的區(qū)分性。通過仿真和實測數(shù)據(jù)驗證，所提方法具有較好檢測性能?；诜抡鏀?shù)據(jù)實驗，當信雜比高于1 dB時，所提方法的檢測概率可以達到60%以上，同時，實測數(shù)據(jù)驗證結果表明，當檢測概率達到80%時，相較于傳統(tǒng)信息幾何檢測器，該文所提檢測器能夠提升檢測信雜比為3～6 dB。
- 雷達目標檢測 /
- 低信雜比 /
- 信息幾何 /
- 矩陣流形 /
- 流形變換
Abstract: A novel and effective information geometry-based method for detecting radar targets is proposed based on the theory of matrix information geometry. Due to the poor discriminative power between the target and the clutter on matrix manifold under complex heterogeneous clutter background with low Signal-to-Clutter Ratio (SCR), in this study, the problem of unsatisfactory performance for the conventional information geometry detector is considered, therefore, to address this issue, a manifold transformation-based information geometry detector is proposed. Concretely, a manifold-to-manifold mapping scheme is designed, and a joint optimization method based on the geometric distance between the Cell Under Test (CUT) and the clutter centroid is presented to enhance the discriminative power between the target and the clutter on the mapped manifold. Finally, the superior performance of the proposed method is evaluated using simulated and real clutter data. The results of simulated data show that the detection probability of the proposed method is over 60% when the SCR exceeds 1 dB. Meanwhile, the real data results confirm that the proposed method can achieve SCR improvement about 3～6 dB compared with the conventional information geometry detector.
- Radar target detection /
- Low signal-to-clutter ratio /
- Information geometry /
- Matrix manifold /
- Manifold transformation

HTML全文

圖 1 雜波概率分布擬合

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圖 2 不同場景下的雜波功率分布

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圖 3 雜波數(shù)據(jù)和目標數(shù)據(jù)的流形分布

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圖 4 基于不同幾何度量的流形變換后分布

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圖 5 K分布雜波背景下，不同方法的檢測性能比較(無干擾)

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圖 6 K分布雜波背景下，不同方法的檢測性能比較(含干擾)

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圖 7 兩組海雜波數(shù)據(jù)功率分布

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圖 8 兩組海雜波數(shù)據(jù)功率譜(第15個距離單元)

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圖 9 基于海雜波數(shù)據(jù) #1不同方法的檢測性能比較

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圖 10 基于海雜波數(shù)據(jù) #2 不同方法的檢測性能比較

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表 1 實測雜波數(shù)據(jù)擬合優(yōu)度檢驗結果

統(tǒng)計分布模型	KL距離	KS統(tǒng)計量
正態(tài)分布	2.33	0.16
對數(shù)正態(tài)分布	0.94	0.11
瑞利分布	4.78	0.23
韋布爾分布	1.56	0.12
K分布	0.78	0.06

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表 2 IPIX雷達數(shù)據(jù)信息

數(shù)據(jù)號	文件名	距離單元數(shù)	脈沖數(shù)
數(shù)據(jù) #1	19980223_190901_ANTSTEP.CDF	34	60000
數(shù)據(jù) #2	19980223_191339_ANTSTEP.CDF	34	60000

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參考文獻(40)

[1]	WATTS S. Cell-averaging CFAR gain in spatially correlated K-distributed clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 1996, 143(5): 321–327. doi: 10.1049/ip-rsn:19960745.
[2]	WANG Sichun, INKOL R, RAJAN S, et al. Performance analysis of the FFT filter bank-based summation CFAR detector[C]. 2008 IEEE Instrumentation and Measurement Technology Conference, Victoria, Canada, 2008: 452–456. doi: 10.1109/IMTC.2008.4547078.
[3]	KELLY E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, AES-22(2): 115–127. doi: 10.1109/TAES.1986.310745.
[4]	LIU Weijian, LIU Jun, HAO Chengpeng, et al. Multichannel adaptive signal detection: Basic theory and literature review[J]. Science China Information Sciences, 2022, 65(2): 121301. doi: 10.1007/s11432-020-3211-8.
[5]	薛健, 朱圓玲, 潘美艷. 基于海雜波先驗知識的雷達目標自適應Rao檢測[J]. 電子與信息學報, 2023, 45(11): 3839–3847. doi: 10.11999/JEIT221216. XUE Jian, ZHU Yuanling, and PAN Meiyan. Adaptive Rao detection of radar targets based on the priori-knowledge of sea clutter[J]. Journal of Electronics & Information Technology, 2023, 45(11): 3839–3847. doi: 10.11999/JEIT221216.
[6]	ROBEY F C, FUHRMANN D R, KELLY E J, et al. A CFAR adaptive matched filter detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208–216. doi: 10.1109/7.135446.
[7]	COLUCCIA A, FASCISTA A, and RICCI G. CFAR feature plane: A novel framework for the analysis and design of radar detectors[J]. IEEE Transactions on Signal Processing, 2020, 68: 3903–3916. doi: 10.1109/TSP.2020.3000952.
[8]	CONTE E, LOPS M, and RICCI G. Asymptotically optimum radar detection in compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(2): 617–625. doi: 10.1109/7.381910.
[9]	許述文, 白曉惠, 郭子薰, 等. 海雜波背景下雷達目標特征檢測方法的現(xiàn)狀與展望[J]. 雷達學報, 2020, 9(4): 684–714. doi: 10.12000/JR20084. XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on the sea surface[J]. Journal of Radars, 2020, 9(4): 684–714. doi: 10.12000/JR20084.
[10]	許述文, 焦銀萍, 白曉惠, 等. 基于頻域多通道圖特征感知的海面小目標檢測[J]. 電子與信息學報, 2023, 45(5): 1567–1574. doi: 10.11999/JEIT220188. XU Shuwen, JIAO Yinping, BAI Xiaohui, et al. Small target detection based on frequency domain multichannel graph feature perception on sea surface[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1567–1574. doi: 10.11999/JEIT220188.
[11]	WU Xijie, DING Hao, LIU Ningbo, et al. A method for detecting small targets in sea surface based on singular spectrum analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5110817. doi: 10.1109/TGRS.2021.3138488.
[12]	GUO Juan, HAO Guocheng, YU Jiantao, et al. Floating target detection in sea clutter via deshape multiple synchrosqueezing transform[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(4): 4285–4294. doi: 10.1109/TAES.2024.3373717.
[13]	尹鵬智, 戴恩澤. 基于變換域分形的海面小目標快速檢測[J]. 電光與控制, 2022, 29(9): 17–21. doi: 10.3969/j.issn.1671-637X.2022.09.004. YIN Pengzhi and DAI Enze. Fast sea surface small target detection based on transform domain fractal[J]. Electronics Optics & Control, 2022, 29(9): 17–21. doi: 10.3969/j.issn.1671-637X.2022.09.004.
[14]	董云龍, 張兆祥, 劉寧波, 等. 海雜波多普勒譜Hurst指數(shù)特性分析及目標檢測[J]. 雷達科學與技術, 2023, 21(4): 355–363,374. doi: 10.3969/j.issn.1672-2337.2023.04.001. DONG Yunlong, ZHANG Zhaoxiang, LIU Ningbo, et al. Characteristic analysis on Hurst index of sea clutter Doppler spectrum and target detection[J]. Radar Science and Technology, 2023, 21(4): 355–363,374. doi: 10.3969/j.issn.1672-2337.2023.04.001.
[15]	BAI Xiaohui, XU Shuwen, ZHU Jianan, et al. Floating small target detection in sea clutter based on multifeature angle variance[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 9422–9436. doi: 10.1109/JSTARS.2023.3321998.
[16]	陳小龍, 何肖陽, 鄧振華, 等. 雷達微弱目標智能化處理技術與應用[J]. 雷達學報, 2024, 13(3): 501–524. doi: 10.12000/JR23160. CHEN Xiaolong, HE Xiaoyang, DENG Zhenhua, et al. Radar intelligent processing technology and application for weak target[J]. Journal of Radars, 2024, 13(3): 501–524. doi: 10.12000/JR23160.
[17]	CHEN Xiaolong, SU Ningyuan, HUANG Yong, et al. False-alarm-controllable radar detection for marine target based on multi features fusion via CNNs[J]. IEEE Sensors Journal, 2021, 21(7): 9099–9111. doi: 10.1109/JSEN.2021.3054744.
[18]	蘇寧遠, 陳小龍, 關鍵, 等. 基于深度學習的海上目標一維序列信號目標檢測方法[J]. 信號處理, 2020, 36(12): 1987–1997. doi: 10.16798/j.issn.1003-0530.2020.12.004. SU Ningyuan, CHEN Xiaolong, GUAN Jian, et al. One-dimensional sequence signal detection method for marine target based on deep learning[J]. Journal of Signal Processing, 2020, 36(12): 1987–1997. doi: 10.16798/j.issn.1003-0530.2020.12.004.
[19]	黎湘, 程永強, 王宏強, 等. 信息幾何理論與應用研究進展[J]. 中國科學: 信息科學, 2013, 43(6): 707–732. doi: 10.1360/112012-424. LI Xiang, CHENG Yongqiang, WANG Hongqiang, et al. Progress in theory and applications of information geometry[J]. SCIENTIA SINICA Informationis, 2013, 43(6): 707–732. doi: 10.1360/112012-424.
[20]	CHENG Yongqiang, HUA Yongqiang, WANG Yongqiang, et al. The geometry of signal detection with applications to radar signal processing[J]. Entropy, 2016, 18(11): 381. doi: 10.3390/e18110381.
[21]	CHEN Xixi, CHENG Yongqiang, WU Hao, et al. Heterogeneous clutter suppression via affine transformation on Riemannian manifold of HPD matrices[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5109813. doi: 10.1109/TGRS.2022.3147494.
[22]	WU Hao, CHENG Yongqiang, CHEN Xixi, et al. Information geometry-based track-before-detect algorithm for slow-moving fluctuating target detection[J]. IEEE Transactions on Signal Processing, 2023, 71: 1833–1848. doi: 10.1109/TSP.2023.3277205.
[23]	ARNAUDON M, BARBARESCO F, and YANG Le. Riemannian medians and means with applications to radar signal processing[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(4): 595–604. doi: 10.1109/JSTSP.2013.2261798.
[24]	YANG Zheng, CHENG Yongqiang, WU Hao, et al. Manifold projection-based subband matrix information geometry detection for radar targets in sea clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5110415. doi: 10.1109/TGRS.2023.3323663.
[25]	WU Hao, CHENG Yongqiang, CHEN Xixi, et al. Geodesic normal coordinate-based manifold filtering for target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5114615. doi: 10.1109/TGRS.2022.3183432.
[26]	楊政, 程永強, 吳昊, 等. 基于正交投影的子帶信息幾何雷達弱小目標檢測方法[J]. 雷達學報, 2023, 12(4): 776–792. doi: 10.12000/JR23079. YANG Zheng, CHENG Yongqiang, WU Hao, et al. Subband information geometry detection method based on orthogonal projection for weak radar targets[J]. Journal of Radars, 2023, 12(4): 776–792. doi: 10.12000/JR23079.
[27]	BARBARESCO F and MEIER U. Radar monitoring of a wake vortex: Electromagnetic reflection of wake turbulence in clear air[J]. Comptes Rendus Physique, 2010, 11(1): 54–67. doi: 10.1016/j.crhy.2010.01.001.
[28]	HUA Xiaoqiang, ONO Y, PENG Linyu, et al. Target detection within nonhomogeneous clutter via total bregman divergence-based matrix information geometry detectors[J]. IEEE Transactions on Signal Processing, 2021, 69: 4326–4340. doi: 10.1109/TSP.2021.3095725.
[29]	HUA Xiaoqiang, CHENG Yongqiang, WANG Yongqiang, et al. Robust covariance estimators based on information divergences and Riemannian manifold[J]. Entropy, 2018, 20(4): 219. doi: 10.3390/e20040219.
[30]	HUA Xiaoqiang, CHENG Yongqiang, WANG Hongqiang, et al. Geometric means and medians with applications to target detection[J]. IET Signal Processing, 2017, 11(6): 711–720. doi: 10.1049/iet-spr.2016.0547.
[31]	HUA Xiaoqiang, CHENG Yongqiang, WANG Hongqiang, et al. Matrix CFAR detectors based on symmetrized Kullback–Leibler and total Kullback–Leibler divergences[J]. Digital Signal Processing, 2017, 69: 106–116. doi: 10.1016/j.dsp.2017.06.019.
[32]	趙文靜, 金明錄, 劉文龍. 基于譜范數(shù)的矩陣CFAR檢測器[J]. 電子學報, 2019, 47(9): 1951–1956. doi: 10.3969/j.issn.0372-2112.2019.09.019. ZHAO Wenjing, JIN Minglu, and LIU Wenlong. Matrix CFAR detector based on matrix spectral norm[J]. Acta Electronica Sinica, 2019, 47(9): 1951–1956. doi: 10.3969/j.issn.0372-2112.2019.09.019.
[33]	王宏強, 程永強, 華小強, 等. 基于流形等距映射的矩陣信息幾何檢測方法[J]. 太赫茲科學與電子信息學報, 2023, 21(4): 523–529. doi: 10.11805/TKYDA2023039. WANG Hongqiang, CHENG Yongqiang, HUA Xiaoqiang, et al. Matrix information geometry method based on manifold ISOMAP[J]. Journal of Terahertz Science and Electronic Information Technology, 2023, 21(4): 523–529. doi: 10.11805/TKYDA2023039.
[34]	HUA Xiaoqiang, PENG Linyu, LIU Weijian, et al. LDA-MIG detectors for maritime targets in nonhomogeneous sea clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5101815. doi: 10.1109/TGRS.2023.3250990.
[35]	IPIX RADAR FILE. IPIX radar dataset files in Grimsby on the shores of Lake Ontari[EB/OL]. http://soma.mcmaster.ca/ipix.php, 2003.
[36]	DO T N, KADDOUM G, NGUYEN T L, et al. Multi-RIS-aided wireless systems: Statistical characterization and performance analysis[J]. IEEE Transactions on Communications, 2021, 69(12): 8641–8658. doi: 10.1109/TCOMM.2021.3117599.
[37]	VAN DER MAATEN L and HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(86): 2579–2605.
[38]	UDRI?TE C. Convex Functions and Optimization Methods on Riemannian Manifolds[M]. Dordrecht, The Netherlands: Springer, 1994. doi: 10.1007/978-94-015-8390-9.
[39]	ABSIL P A, MAHONY R, and SEPULCHRE R. Optimization Algorithms on Matrix Manifolds[M]. Princeton, USA: Princeton University Press, 2008. doi: 10.1515/9781400830244.
[40]	ZHAO Wenjing, LIU Chang, LIU Wenlong, et al. Maximum eigenvalue-based target detection for the K-distributed clutter environment[J]. IET Radar, Sonar & Navigation, 2018, 12(11): 1294–1306. doi: 10.1049/iet-rsn.2018.5229.