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The covariance approximation (CA) multiple signal classification (MUSIC) is a novel near-field direction-of-arrival (DoA) estimation method for uniform linear array. In this paper, we show that the CA–MUSIC suffers from significant performance degeneration caused by coherent sources. The CA–MUSIC with coherent sources generates the image sources (IS), which cannot be distinguished from the real sources. To solve this problem, we propose a CA-based near-field coherent sources localization algorithm, which is robust to the IS effect. The proposed CA algorithm avoids errors caused by coherence between sources using searching radius restriction and zero-forcing MUSIC. Simulation results shows that the proposed CA algorithm offers superior root mean square error (RMSE) performances for near-field coherent sources.