Target detection is a challenging problem in the complex detection environment of high-frequency surface wave radar (HFSWR). Due to the existence of various clutter and interferences, the detection environment in HFSWR is highly non-homogeneous with sharp/smooth clutter edges and multiple interfering targets. In this paper, we propose an effective detector for the non-homogeneous Weibull clutter in HFSWR with the use of prior information on the environmental context. Considering the spatial information of distribution parameters among neighboring cells and sparse information of interfering targets, the proposed method uses the Bayesian method to estimate distribution parameters and designs an objective function, which is composed of the data fidelity term, block matching 3-D frames and a sparsity regularization term. With the estimates of distribution parameters, the detection threshold is calculated accordingly and the presence of the target cell is decided. By minimizing the proposed model, the proposed method provides robust and accurate estimates of distribution parameters and can achieve the root-mean-squared error less than 0.12 in the simulated detection scenarios. Simulation and experimental results from a real HFSWR system are given to demonstrate the effectiveness of the proposed detector.

在高频表面波雷达（HFSWR）的复杂检测环境中，目标检测是一个具有挑战性的问题。由于各种杂波和干扰的存在，HFSWR的检测环境高度不均匀，杂波边缘清晰/光滑，并且有多个干扰目标。在本文中，我们使用有关环境的先验信息，提出了一种用于HFSWR中非均匀Weibull杂波的有效检测器。考虑到相邻小区之间分布参数的空间信息和干扰目标的稀疏信息，该方法采用贝叶斯方法估计分布参数，并设计了目标函数，该函数由数据保真度项，块匹配3-D帧和稀疏正则化术语。利用分布参数的估计，相应地计算检测阈值并确定目标细胞的存在。通过最小化所提出的模型，所提出的方法提供了分布参数的鲁棒且准确的估计，并且在模拟的检测场景中可以实现小于0.12的均方根误差。给出了来自实际HFSWR系统的仿真和实验结果，以证明所提出的探测器的有效性。