Spread-Doppler clutter mitigation based on ionospheric irregularity learning for skywave radar

摘要

The detection performance of skywave radar on slow moving targets is essentially limited by the energy of spread-Doppler clutter (SDC). Various factors may lead to SDC. It is hard to adopt a single mitigation strategy for all situations. The idea of different signal processing strategies for different regions is needed to solve such a complex problem. The regions need to be divided first to be processed using the knowledge-aided (KA) approach. The knowledge of different regions can be learned either from auxiliary devices or from data in the radar itself. Adaptive beamforming is rarely used to mitigate sea clutter due to its omnidirectional nature. However, the existence of ionospheric irregularity may give rise to spatial structures in sea clutter. In this work, we demonstrated the use of adaptive beamforming for SDC mitigation for skywave radar when ionospheric irregularity region was learned using correlation analysis with a KA approach. The performance of this approach was further validated by real data processing results.