Target-to-Target Interaction Ghost Suppression Algorithm Based on Joint DoD and DoA Estimation

Abstract

In practical multitarget scenarios, the targets will interact with each other, yielding undesired multipath signals. These signals, appearing as “ghosts” in radar images, can severely hinder reliable target identification. In this article, we propose a novel approach for suppressing multipath ghosts induced by target-to-target interactions based on joint estimation of the Direction of Departure (DoD) and Direction of Arrival (DoA) using the multiple-input-multiple-output (MIMO) radar. Specifically, first, the multipath signal model for multitarget scenarios, which accounts for target-to-target interactions, is established. Then, based on the ellipse-cross-localization (ECL) method and geometric relationships, the unique features of ghosts, including defocusing, DoD, and DoA features, are explored. Inspired by these discriminative features, we perform joint DoD and DoA estimation on the echoes corresponding to candidate targets and introduce a new criterion based on the DoD-DoA heat maps for ghost identification. Following ghost identification, the ghost-free image is obtained by applying a weighting function to filter out the ghost echoes from the received signals. Finally, simulation and experimental results demonstrate that the proposed method achieves good performance in suppressing ghosts across different scenarios and varying signal-to-noise ratio (SNR) conditions.