基于概率定义扩展样本的机载雷达空间和时间相关海杂波数据仿真方法

doi:10.3969/j.issn.1000-1093.2019.03.011

兵工学报 ›› 2019, Vol. 40 ›› Issue (3): 530-538.doi: 10.3969/j.issn.1000-1093.2019.03.011

基于概率定义扩展样本的机载雷达空间和时间相关海杂波数据仿真方法

毛辉煌¹，谢文冲¹，徐鹏¹，刘畅²

(1.空军预警学院，湖北武汉 430019; 2.中国人民武装警察部队警官学院，四川成都 610000）

收稿日期:2018-04-25 修回日期:2018-04-25 上线日期:2019-04-29
作者简介:毛辉煌（1994—），男，硕士研究生。E-mail: sparksmhh@163.com
基金资助:
国家自然科学基金项目（61501506）

A Method Based on Probability Definition with Extended Samples to Generate Spatial-temporal Correlated Sea Clutter forAirborne Radar

MAO Huihuang¹, XIE Wenchong¹, XU Peng¹, LIU Chang²

（1.Airforce Early Warning Academy， Wuhan 430019, Hubei, China; 2.Officers College of PAP, Chengdu 610000, Sichuan, China）

Received:2018-04-25 Revised:2018-04-25 Online:2019-04-29

摘要/Abstract

摘要： 海杂波背景下的目标检测性能是机载预警雷达的重要指标之一。为了给机载雷达海杂波抑制方案的有效设计提供数据来源，需要仿真模拟出满足实际分布特性和相关性的海杂波。针对实测海杂波数据具有的显著时间相关性和空间相关性，提出了一种新的海杂波数据产生方法。该方法基于概率密度定义及相关性的本质，运用蒙特卡罗思想和Cholesky分解来产生空间和时间相关的海杂波。这种方法不仅简化了海杂波数据产生流程，同时提高了海杂波幅度和相关性吻合效果。分别利用实测和仿真数据验证了所提方法的有效性。

关键词: 机载雷达, 海杂波, 数据仿真, 空间和时间相关, 实测数据

Abstract: Target detection performance in the background of sea clutter is one of the important indicators of airborne early-warning radar. The sea clutter with actual distribution characteristics and correlation are simulated to provide data sources for the effective design of airborne radar sea clutter suppression schemes. For the significant temporal correlation and spatial correlation of measured sea clutter data, a new method is proposed for generating sea clutter data. The proposed method is based on the definition of probability density and the nature of correlation. Monte Carlo method and Cholesky decomposition are used to generate spatial-temporal correlated sea clutter. The proposed method can be used to simplify the generation process of sea clutter data and improve the amplitude and correlation of sea clutter. The effectiveness of the proposed method is verified by using the measured and simulated data. Key

Key words: airborneradar, seaclutter, datasimulation, spatial-temporalcorrelation, measureddata

中图分类号:

TN959.73

毛辉煌，谢文冲，徐鹏，刘畅. 基于概率定义扩展样本的机载雷达空间和时间相关海杂波数据仿真方法[J]. 兵工学报, 2019, 40(3): 530-538.

MAO Huihuang, XIE Wenchong, XU Peng, LIU Chang. A Method Based on Probability Definition with Extended Samples to Generate Spatial-temporal Correlated Sea Clutter forAirborne Radar[J]. Acta Armamentarii, 2019, 40(3): 530-538.

参考文献

［1］ RAYNAL A M, DOERRY A W. Doppler characteristics of sea clutter[R].Springfield, VA, US: National Technical Information Service, US Department of Commerce,2010.
[2］ FARSHCHIAN M. Characterization of sea clutter amplitude and Doppler bin PDFs[R]. Washington DC, US:Naval Research Laboratory, 2014.
[3］ MCDONALD M, CERUTTI-MAORI D. Multi-phase centre coherent radar sea clutter modelling and simulation[J]. IET Radar, Sonar & Navigation, 2017, 11(9):1359-1366.
[4］ ROSENBERG L, BOCQUET S. Non-coherent radar detection performance in medium grazing angle X-band sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017,53(2):669-682.
[5］ ROSENBERG L, WATTS S. Coherent detection in medium grazing angle sea-clutter[J]. IET Radar, Sonar & Navigation, 2017, 11(9):1340-1348.
[6］ WISE G L, TRAGANITIS A P, THOMAS J B. The effect of a memoryless nonlinearity on the spectrum of a random process[J]. IEEE Transactions on Information Theory, 1977, 23(1): 84-89.
[7］ CONTE E, LONGO M, LOPS M. Modeling and simulation of non-Rayleigh radar clutter[J]. IEE Proceedings—Part F:Radar and Signal Processing, 1991, 138(2): 121-130.
[8］周万幸.基于SIRP的时空相关K-分布海杂波仿真研究[J].电子学报，2009，37（12）：2672-2676.
ZHOU W X.Study of spatial-temporal correlated K-distribution sea clutter simulation based on SIRP[J]. Acta Electronica Sinca, 2009, 37(12):2672-2676.(in Chinese)
[9］ TENG L, DAN H, DI Y. Model for spatial-correlated clutter and its application to temporal-spatial correlated clutter[J]. IET Microwaves Antennas & Propagation, 2011, 5(3):298-304.

[10］施龙飞，王雪松，徐振海，等. 空间二维相关K分布杂波建模与仿真[J].信号处理，2005，21（3）：322-326.
SHI L F, WANG X S, XU Z H,et al. Modeling and simulation of spatially bidimensional correlated K-distributed radar clutter[J]. Signal Processing, 2005, 21(3): 322-326.(in Chinese)
[11］关键，丁昊，黄勇，等.实测海杂波数据空间相关性研究[J].电波科学学报，2012，27（5）：943-953.
GUAN J, DING H, HUANG Y, et al. Spatial correlation property with measured sea clutter data[J]. Journal of Radio Science, 2012, 27(5): 943-953.(in Chinese)
[12］ MADAR V, DATTA S, KOUL H L. Direct formulation to Cholesky decomposition of a general nonsingular correlation matrix[J]. Statistics & Probability Letters, 2015, 103:142-147.
[13］魏明珠. 机载预警雷达海杂波的抑制方法[J].雷达科学与技术，2015，13（6）：627-638.
WEI M Z.Study on sea clutter suppression for airborne early warning radar[J].Radar Science and Technology,2015，13（6）：627-638.(in Chinese)

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基于概率定义扩展样本的机载雷达空间和时间相关海杂波数据仿真方法

A Method Based on Probability Definition with Extended Samples to Generate Spatial-temporal Correlated Sea Clutter forAirborne Radar

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