In this article, a spatial-temporally varying generalized Pareto intensity distribution (STV-GPID) model is presented to characterize large-scene sea clutter in high-resolution maritime surveillance radars, and a multiscan recursive Bayesian bipercentile (MSRB-BiP) estimation method is proposed to implement the outlier-robust estimation of parameters in the STV-GPID model. Considering that sea clutter characteristics are affected by sea states and the viewing geometry of a radar, the large scene is segmented into clutter map cells based on an empirical backscattering coefficient model of sea surface to predict radar cross section (RCS) of sea surface per unit physical area. Sea clutter intensities on each clutter map cell are modeled by a GPID. In the parameter estimation, the data of previous scans are transformed into the prior information on the parameters to reduce the storage burden of radar systems. The MSRB-BiP estimator updates the parameters of the STV-GPID model recursively by a mixed sample set with the returns of the present scan and simulated data using the prior information. The mixture ratio adjusts the forgetting rate of data to adapt to temporally varying characteristics of sea clutter. At least, it brings three merits: low storage requirement, outlier robustness, and mitigation of spatial small sample size of a single scan. The convergence and robustness of the estimation method are verified by simulated data. The experimental results on two measured radar datasets verify the effectiveness of the MSRB-BiP estimators, and the errors at the steady state are reduced at least 17.7% and 66.7%, respectively.

中文翻译：

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海杂波大场景时空变化广义帕累托分布模型的多扫描递归贝叶斯参数估计

本文提出了一种时空变化的广义帕累托强度分布（STV-GPID）模型来表征高分辨率海上监视雷达中的大场景海杂波，并提出了一种多扫描递归贝叶斯双百分位数（MSRB-BiP）估计方法。提出在 STV-GPID 模型中实现参数的异常稳健估计。考虑到海杂波特性受海况和雷达视场几何的影响，基于海面经验后向散射系数模型将大场景分割成杂波图单元，预测单位海面的雷达截面（RCS）物理区域。每个杂波图单元上的海杂波强度由 GPID 建模。在参数估计中，将前次扫描的数据转化为参数的先验信息，以减轻雷达系统的存储负担。MSRB-BiP 估计器通过混合样本集递归地更新 STV-GPID 模型的参数，其中包含当前扫描的返回和使用先验信息的模拟数据。混合比调整数据的遗忘率，以适应海杂波随时间变化的特性。至少，它带来了三个优点：低存储要求、异常值鲁棒性和减轻单次扫描的空间小样本量。通过模拟数据验证了估计方法的收敛性和鲁棒性。在两个实测雷达数据集上的实验结果验证了 MSRB-BiP 估计器的有效性，稳态时的误差分别降低了至少 17.7% 和 66.7%，