基于多特征量的海杂波参数估计

doi:10.3969/j.issn.1000-1093.2020.03.012

摘要/Abstract

摘要： 研究分析海杂波对于有效评估不同海况下的弹载合成孔径雷达（SAR）成像性能具有重大意义。针对传统参数估计法较难精确拟合海杂波幅度分布的峰值、幅度宽度等多个特征量的问题，提出基于多特征量的参数估计法。利用海杂波的幅度分布特性和时间相关性，建立基于球不变随机过程法的海杂波模型；构造反映海杂波幅度分布的4个特征量，即概率最大处幅度、概率最大处幅度的分布概率、半概率幅度宽度、分布概率小于0.01的幅度临界点，利用反向传播（BP）神经网络建立尺度参数、形状参数与4个特征量之间的定量关系，决定系数分别为0.992、0.994；基于实测数据和训练好的BP神经网络求解尺度参数、形状参数，建立海杂波幅度分布模型，并与经验公式和混合估计法进行对比；通过分析不同海况下的后向散射系数确定海杂波的信号功率，为评估不同海况下的弹载SAR成像性能提供了海杂波模型。

关键词: 海杂波, 多特征量, 弹载合成孔径雷达, 参数估计

Abstract: The sea clutter is studied and analyzed to effectively evaluate the imaging performance of missile-borne synthetic aperture radar (SAR) under different sea conditions. In view of the difficulty in accurately fitting some characteristic quantities, such as peak value and amplitude width of sea clutter amplitude distribution, by traditional parameter estimation method, a parameter estimation method based on multiple characteristic quantities is proposed. The amplitude distribution characteristics and temporal correlation of sea clutter are used to establish a sea clutter model based on SIRP method. Four characteristic quantities, including magnitude with the highest probability, magnitude distribution probability with the highest probability, amplitude width with half of probability, and amplitude critical point with half of probability, which reflect the amplitude distribution of sea clutter, are constructed, and the quantitative relations among scale parameter, shape parameter and four characteristic quantities are established by using BP neural network. The determination coefficients are 0.992 and 0.994, respectively. On this basis, the measured data and the trained BP neural network are used to solve the scale and shape parameters, and an amplitude distribution model of sea clutter is established, which is compared with empirical formula and mixed estimation method. The signal power of sea clutter is determined by analyzing the back-scattering coefficient under different sea conditions, so as to provide a sea clutter model for evaluating the imaging performance of missile-borne SAR under different sea conditions. Key

Key words: seaclutter, multiplecharacteristicquantity, missile-bornesyntheticapertureradar, parameterestimation

中图分类号:

TN959.1⁺7

何耀民，何华锋，徐永壮，王依繁，苏敬. 基于多特征量的海杂波参数估计[J]. 兵工学报, 2020, 41(3): 517-525.

HE Yaomin， HE Huafeng， XU Yongzhuang， WANG Yifan， SU Jing. Estimation of Sea Clutter Parameters Based on Multiple Characteristic Quantities[J]. Acta Armamentarii, 2020, 41(3): 517-525.

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第41卷第3期2020 年3月
兵工学报ACTA ARMAMENTARII
Vol.41No.3Mar.2020

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