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The problem that this paper is concerned with is High Frequency Surface Wave Radar (HFSWR) detection of desired targets against a complex interference background consisting of sea clutter, ionosphere clutter, Radio Frequency Interference (RFI) and atmospheric noise. Eliminating unwanted echoes and exploring obscured targets contribute to achieving ideal surveillance of sea surface targets. In this paper, a Self-regulating Multi-clutter Suppression Framework (SMSF) has been proposed for small aperture HFSWR. SMSF can remove many types of clutter or RFI; meanwhile, it mines the targets merged into clutter and tracks the travelling path of the ship. In SMSF, a novel Dynamic Threshold Mapping Recognition (DTMR) method is first proposed to reduce the atmospheric noise and recognize each type of unwanted echo; these recognized echoes are fed into the proposed Adaptive Prophase-current Dictionary Learning (APDL) algorithm. To make a comprehensive evaluation, we also designed three novel assessment parameters: Obscured Targets Detection Rate (OTDR), Clutter Purification Rate (CPR) and Erroneous Suppression Rate (ESR). The experiment data collected from a small aperture HFSWR system confirm that SMSF has precise suppression performance over most of the classical algorithms and concurrently reveals the moving targets, and OTDR of SMSF is usually higher than compared methods.

A Self-Regulating Multi-Clutter Suppression Framework for Small Aperture HFSWR Systems