李重威, 杨勇, 李永祯, 李超. 基于实测数据的分时全极化雷达海杂波分布研究[J]. 电波科学学报, 2017, 32(3): 253-260. doi: 10.13443/j.cjors.2017032201
      引用本文: 李重威, 杨勇, 李永祯, 李超. 基于实测数据的分时全极化雷达海杂波分布研究[J]. 电波科学学报, 2017, 32(3): 253-260. doi: 10.13443/j.cjors.2017032201
      LI Zhongwei, YANG Yong, LI Yongzhen, LI Chao. Time-sharing fully polarimetric radar sea clutter distribution based on real measured data[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2017, 32(3): 253-260. doi: 10.13443/j.cjors.2017032201
      Citation: LI Zhongwei, YANG Yong, LI Yongzhen, LI Chao. Time-sharing fully polarimetric radar sea clutter distribution based on real measured data[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2017, 32(3): 253-260. doi: 10.13443/j.cjors.2017032201

      基于实测数据的分时全极化雷达海杂波分布研究

      Time-sharing fully polarimetric radar sea clutter distribution based on real measured data

      • 摘要: 首先基于IPIX分时全极化雷达的海杂波实测数据,分析了瑞利分布、韦布尔分布、对数正态分布和K分布对实测数据的拟合效果,发现对数正态分布拟合水平极化接收通道的杂波数据效果较好,K分布拟合垂直极化接收通道的杂波数据效果较好,但是仍然存在海杂波"拖尾"区域拟合不理想的情况.为此,假设海杂波中的布拉格散射体的回波服从K分布,离散海尖峰的回波服从对数正态分布,海杂波整体服从K分布和对数正态分布的叠加混合分布,提出了一种具有闭式表达式的混合分布,以改善海杂波分布"拖尾"部分的拟合效果.实测数据分析表明,本文提出的混合分布对海杂波幅度分布的拟合效果优于对数正态分布和K分布.

         

        Abstract: Based on sea clutter real measured data of IPIX radar, the Rayleigh, Weibull, Log-normal and K-distribution are used to fit the distribution of sea clutter. It shows Log-normal distribution fitting the horizontal polarization to receive better and K-distribution fitting the vertical polarization to receive better, but there is still a situation that the sea clutter trailing region. In this paper, we assume that the Bragg scatterers in the sea clutter follow the K-distribution, and the discrete spikes obey the Log-normal distribution, the mixed distributions of the sea clutter as a whole obeys the K-distribution and the Log-normal distribution superimposed. A mixed distribution with closed expression is proposed, which improves the heavy tailing problem in fully polarimetric scattering characteristic sea clutter modeling. It is verified by the earl measured data that the mixed distribution has better fitting effect than Log-normal distribution and K-distribution.

         

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