Recently, high-frequency surface wave radar (HFSWR) has been widely applied in ocean surface dynamic parameter measurement. However, the radar echoes backscattered from ocean surface tend to be contaminated by the external radio frequency interference (RFI), and the mitigation of RFI becomes an intractable problem, especially for wide beam HFSWR. The HFSWR measured data are multichannel. The higher-order singular value decomposition (HOSVD) is an effective method to improve the accuracy of subspace estimation by exploiting this multidimensional structure. In this article, we develop an RFI mitigation method based on the HOSVD algorithm and orthogonal subspace projection. Simulations indicate that, compared with the previous orthogonal subspace projection RFI cancellation schemes, the proposed method has significant advantages in keeping the desired signals while suppressing the interference. The proposed method is applied to the experimental data of the HFSWR with severe RFI. The ocean surface currents inverted from the RFI-mitigated data agree reasonably with the tidal features in the radar detection area. After RFI mitigation, the performance of the HFSWR system is significantly improved on the effective current detection range and precision.

中文翻译：

---

一种基于高阶奇异值分解的高频面波雷达射频干扰抑制方法

近年来，高频面波雷达（HFSWR）在海面动力参数测量中得到了广泛的应用。然而，从海面背向散射的雷达回波容易受到外部射频干扰（RFI）的污染，RFI的抑制成为一个棘手的问题，特别是对于宽波束HFSWR。HFSWR 测量数据是多通道的。高阶奇异值分解（HOSVD）是利用这种多维结构提高子空间估计精度的有效方法。在本文中，我们开发了一种基于 HOSVD 算法和正交子空间投影的 RFI 缓解方法。仿真表明，与之前的正交子空间投影 RFI 消除方案相比，所提出的方法在抑制干扰的同时保持所需信号具有显着优势。将所提出的方法应用于具有严重 RFI 的 HFSWR 的实验数据。从 RFI 缓解数据反演的海面洋流与雷达探测区域的潮汐特征合理吻合。RFI 抑制后，HFSWR 系统的性能在有效电流检测范围和精度上得到显着提高。