New BSS‐based ABF for heterogeneous ionospheric clutter mitigation in HFSWR

Abstract

Ionospheric clutter is detrimental to target detection in high-frequency surface-wave radar (HFSWR). Adaptive beamforming (ABF) has been adopted to suppress the ionospheric clutter in recent years. Unfortunately, the performance of ABF degrades due to the heterogeneity of the ionospheric clutter. In this study, a new approach is proposed to suppress the ionospheric clutter. A novel heterogeneous ionospheric clutter model based on the mixture of mutually independent ionospheric clutter sources is established. The blind source separation (BSS) approach is used to separate the clutter sources first. Then the separated components are exploited to estimate the spatial covariance matrix (SCM) of the cell under test. The SCM estimation based on BSS is more accurate than the classical sample matrix inversion method under the heterogeneous ionospheric clutter. Therefore, the ABF performance improves due to the better SCM estimation. The simulation and real data results demonstrated the effectiveness of the new BSS-based ABF method in HFSWR. The signal-to-clutter-plus-noise ratio of the new method improved compared with the traditional ABF based on the SMI method. The new method can help to detect targets under the heterogeneous ionospheric clutter. It may also be a promising methodology in other ABF applications.