基于海雜波先驗(yàn)知識(shí)的雷達(dá)目標(biāo)自適應(yīng)Rao檢測(cè)

薛健; 朱圓玲; 潘美艷

doi:10.11999/JEIT221216

基于海雜波先驗(yàn)知識(shí)的雷達(dá)目標(biāo)自適應(yīng)Rao檢測(cè)

doi: 10.11999/JEIT221216

1.
西安郵電大學(xué)通信與信息工程學(xué)院西安 710121
2.
西安電子工程研究所西安 710100

基金項(xiàng)目: 國(guó)家自然科學(xué)基金 (62201455)，陜西省教育廳科研計(jì)劃 (22JK0566)

詳細(xì)信息

作者簡(jiǎn)介:
薛?。耗?，副教授，研究方向?yàn)榈睾ｋs波抑制、雷達(dá)目標(biāo)檢測(cè)等

朱圓玲：女，碩士生，研究方向?yàn)槔走_(dá)目標(biāo)檢測(cè)

潘美艷：女，工程師，研究方向?yàn)殡s波智能抑制、雷達(dá)目標(biāo)識(shí)別等

通訊作者:
薛健　jxue@xupt.edu.cn

中圖分類(lèi)號(hào): TN957.51
計(jì)量
- 文章訪問(wèn)數(shù): 844
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2022-09-19
- 修回日期: 2022-10-30
- 網(wǎng)絡(luò)出版日期: 2022-11-03
- 刊出日期: 2023-11-28

Adaptive Rao Detection of Radar Targets Based on the Priori-Knowledge of Sea Clutter

1.
School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Xi'an Electronic Engineering Research Institute, Xi’an 710100, China

Funds: The National Natural Science Foundation of China (62201455), The Scientific Research Program Funded by Shaanxi Provincial Education Department (22JK0566)

摘要

摘要: 針對(duì)非高斯非均勻海雜波背景下雷達(dá)海面目標(biāo)檢測(cè)性能改善的問(wèn)題，該文基于海雜波的先驗(yàn)知識(shí)提出了一種自適應(yīng)Rao雷達(dá)目標(biāo)檢測(cè)方法。首先將海雜波的紋理分量和散斑協(xié)方差矩陣分別建模為逆高斯隨機(jī)變量和逆復(fù)Wishart分布的隨機(jī)矩陣，然后基于Rao檢驗(yàn)和未知參數(shù)估計(jì)，設(shè)計(jì)了一種匹配海雜波特性的雷達(dá)目標(biāo)自適應(yīng)Rao檢測(cè)方法。通過(guò)理論推導(dǎo)和實(shí)驗(yàn)驗(yàn)證了所提檢測(cè)方法對(duì)雜波平均功率和協(xié)方差均值矩陣具有恒虛警特性。仿真數(shù)據(jù)和實(shí)測(cè)數(shù)據(jù)實(shí)驗(yàn)結(jié)果表明，在非高斯非均勻環(huán)境下所提檢測(cè)方法優(yōu)于已有檢測(cè)方法，并且具有良好的魯棒性。
- 雷達(dá)目標(biāo) /
- 自適應(yīng)Rao檢測(cè) /
- 海雜波 /
- 先驗(yàn)知識(shí)
Abstract: An adaptive Rao detection method for radar targets is proposed based on the priori knowledge of sea clutter to improve the radar’s target detection performance in non-Gaussian and nonhomogeneous sea clutter. First, the texture component and the speckle covariance matrix of sea clutter are modeled as an inverse Gaussian random variable and an inverse complex Wishart random matrix, respectively. Then, an adaptive Rao detection method for radar targets, with quite similar characteristics as sea clutter, is designed based on the Rao test and unknown parameter estimation. The detection method is verified by theoretical derivation and experiments in demonstrating constant false alarm characteristics for the mean power and covariance mean matrix of sea clutter. The experimental results of the simulated and experimental data reveal that the proposed detection method outperforms existing detection methods in non-Gaussian and nonhomogeneous sea clutter environments with good robustness.
- Radar targets /
- Adaptive Rao detection /
- Sea clutter /
- Priori knowledge

HTML全文

圖 1 不同形狀參數(shù)下檢測(cè)器性能對(duì)比圖

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圖 2 不同參考單元數(shù)量下檢測(cè)器性能對(duì)比圖

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圖 3 不同自由度參數(shù)下KA-RAO的檢測(cè)概率曲線圖

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圖 4 目標(biāo)歸一化多普勒頻率失配對(duì)提出檢測(cè)器性能的影響

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圖 5 實(shí)測(cè)海雜波數(shù)據(jù)幅度特性分析及檢測(cè)器的檢測(cè)概率曲線圖

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圖 6 實(shí)測(cè)海雜波數(shù)據(jù)TFC15-005幅度特性分析及檢測(cè)器的檢測(cè)概率曲線圖

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圖 7 KA-RAO檢測(cè)器的虛警概率曲線

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參考文獻(xiàn)(24)

[1]	劉寧波, 姜星宇, 丁昊, 等. 雷達(dá)大擦地角海雜波特性與目標(biāo)檢測(cè)研究綜述[J]. 電子與信息學(xué)報(bào), 2021, 43(10): 2771–2780. doi: 10.11999/JEIT200451 LIU Ningbo, JIANG Xingyu, DING Hao, et al. Summary of research on characteristics of radar sea clutter and target detection at high grazing angles[J]. Journal of Electronics &Information Technology, 2021, 43(10): 2771–2780. doi: 10.11999/JEIT200451
[2]	BANDIERA F, ORLANDO D, and RICCI G. Advanced Radar Detection Schemes Under Mismatched Signal Models[M]. Cham: Springer, 2009: 1–105.
[3]	LIU Jun, HAN Jinwang, ZHANG Zijing, et al. Bayesian detection for MIMO radar in Gaussian clutter[J]. IEEE Transactions on Signal Processing, 2018, 66(24): 6549–6559. doi: 10.1109/TSP.2018.2879038
[4]	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
[5]	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
[6]	WARD K D. Compound representation of high resolution sea clutter[J]. Electronics Letters, 1981, 17(16): 561–563. doi: 10.1049/el:19810394
[7]	WATTS S. Radar detection prediction in sea clutter using the compound K-distribution model[J]. IEE Proceedings F (Communications, Radar and Signal Processing), 1985, 132(7): 613–620. doi: 10.1049/ip-f-1.1985.0115
[8]	MEZACHE A, SOLTANI F, SAHED M, et al. Model for non-Rayleigh clutter amplitudes using compound inverse Gaussian distribution: An experimental analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 142–153. doi: 10.1109/TAES.2014.130332
[9]	水鵬朗, 田超, 封天. 逆高斯紋理復(fù)合高斯雜波對(duì)異常樣本穩(wěn)健的三分位點(diǎn)估計(jì)方法[J]. 電子與信息學(xué)報(bào), 待發(fā)表. S H U I P e n g l a n g , T I A N C h a o , a n d F E N G T i a n . O u t l i e r - r o b u s t t r i - p e r c e n t i l e p a r a m e t e r e s t i m a t i o n m e t h o d o f c o m p o u n d - G a u s s i a n c l u t t e r w i t h i n v e r s e G a u s s i a n t e x t u r e s [ J ] . < i > J o u r n a l < / i > < i > o f < / i > < i > E l e c t r o n i c s < / i > & a m p ; < i > I n f o r m a t i o n < / i > < i > T e c h n o l o g y < / i > , T o b e p u b l i s h . d o i :
[10]	SHUI Penglang, ZOU Pengjia, and FENG Tian. Outlier-robust truncated maximum likelihood parameter estimators of generalized Pareto distributions[J]. Digital Signal Processing, 2022, 127: 103527. doi: 10.1016/j.dsp.2022.103527
[11]	FENG Tian and SHUI Penglang. Outlier-robust tri-percentile parameter estimation of compound-Gaussian clutter with lognormal distributed texture[J]. Digital Signal Processing, 2022, 120: 103307. doi: 10.1016/j.dsp.2021.103307
[12]	KONG Lingjiang, LI Na, CUI Guolong, et al. Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture[J]. IET Radar, Sonar & Navigation, 2016, 10(4): 689–698. doi: 10.1049/iet-rsn.2015.0241
[13]	韓金旺, 張子敬, 劉軍, 等. 基于貝葉斯的高斯雜波背景下MIMO雷達(dá)自適應(yīng)檢測(cè)算法[J]. 雷達(dá)學(xué)報(bào), 2019, 8(4): 501–509. doi: 10.12000/JR18090 HAN Jinwang, ZHANG Zijing, LIU Jun, et al. Adaptive Bayesian detection for MIMO radar in Gaussian clutter[J]. Journal of Radars, 2019, 8(4): 501–509. doi: 10.12000/JR18090
[14]	XUE Jian, XU Shuwen, LIU Jun, et al. Bayesian detection for radar targets in compound-Gaussian sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4020805. doi: 10.1109/LGRS.2022.3140727
[15]	OLLILA E, TYLER D E, KOIVUNEN V, et al. Compound-Gaussian clutter modeling with an inverse Gaussian texture distribution[J]. IEEE Signal Processing Letters, 2012, 19(12): 876–879. doi: 10.1109/LSP.2012.2221698
[16]	SUN Mengru, LIU Weijian, LIU Jun, et al. Complex parameter Rao, Wald, gradient, and Durbin tests for multichannel signal detection[J]. IEEE Transactions on Signal Processing, 2022, 70: 117–131. doi: 10.1109/TSP.2021.3132485
[17]	LIU Weijian, ZHANG Zhaojian, LIU Jun, et al. Detection of a rank-one signal with limited training data[J]. Signal Processing, 2021, 186: 108120. doi: 10.1016/j.sigpro.2021.108120
[18]	SHUI Penglang, SHI Lixiang, YU Han, et al. Iterative maximum likelihood and outlier-robust bipercentile estimation of parameters of compound-Gaussian clutter with inverse Gaussian texture[J]. IEEE Signal Processing Letters, 2016, 23(11): 1572–1576. doi: 10.1109/LSP.2016.2605129
[19]	SVENSSON L and LUNDBERG M. On posterior distributions for signals in Gaussian noise with unknown covariance matrix[J]. IEEE Transactions on Signal Processing, 2005, 53(9): 3554–3571. doi: 10.1109/TSP.2005.853102
[20]	TAGUE J A and CALDWELL C I. Expectations of useful complex Wishart forms[J]. Multidimensional Systems and Signal Processing, 1994, 5(3): 263–279. doi: 10.1007/BF00980709
[21]	許述文, 王喆祥, 水鵬朗. 海雜波背景下雷達(dá)目標(biāo)貝葉斯檢測(cè)算法[J]. 西安電子科技大學(xué)學(xué)報(bào), 2021, 48(2): 15–26. doi: 10.19665/j.issn1001-2400.2021.02.003 XU Shuwen, WANG Zhexiang, and SHUI Penglang. Knowledge-based adaptive detection of radar targets in sea clutter background[J]. Journal of Xidian University, 2021, 48(2): 15–26. doi: 10.19665/j.issn1001-2400.2021.02.003
[22]	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
[23]	XUE Jian, XU Shuwen, and SHUI Penglang. Knowledge-based target detection in compound Gaussian clutter with inverse Gaussian texture[J]. Digital Signal Processing, 2019, 95: 102590. doi: 10.1016/j.dsp.2019.102590
[24]	HERSELMAN P L and BAKER C J. Analysis of calibrated sea clutter and boat reflectivity data at C- and X-band in South African coastal waters[C]. Proceedings of the IET International Conference on Radar Systems 2007, Edinburgh, UK, 2007: 1–5.