Acoustic environment has a low signal-to-noise ratio (SNR); hence, array signal processing is widely used for noise reduction and signal enhancement. The actual ambient noise includes uncorrelated noise and correlated noise. The received noises of the two arbitrary array elements are correlated. Consequently, the performance of array signal processing method decreases obviously. Aiming at this problem, the real-part elimination of covariance matrix method is proposed. Firstly, from a physical point of view, the noise signals can be generated by using a number of uncorrelated noise sources: the more the noise sources, the less the error between the noise from the model and the actual noise will be. Theoretically, the noise field is decomposed into the symmetrical noise field and the asymmetrical noise field. A number of noise sources generate the symmetrical noise fields; the directions of these noise sources are symmetric, and the powers of two arbitrary symmetric sources are the same. Secondly, the symmetry of the ambient noise is analyzed, as a result, the symmetrical noise can only affect the real part of the covariance matrix. Thirdly, the real part of covariance matrix is eliminated in order to reduce the noise, and then the delay-and-sum beamforming is achieved by using only the imaginary part. The advantages are that the output signal-to-noise ratio is increased and the noise output power is reduced obviously;the disadvantage is that it produces a false target. The azimuth of the actual target differs from that of the false target by 180◦, and the false target cannot be distinguished. Finally, to eliminate the false target, the real part of the signal covariance matrix is reconstructed by establishing a constrained optimization problem, which is solved by using the particle swarm algorithm. Then, the reconstructed covariance matrix composed of the imaginary part and the reconstruction of real part is applied to delay-and-sum beamforming, as a result, the false target is eliminated. The simulation results show that the real-part elimination of covariance matrix method reduces the symmetrical ambient noise, the noise output power is reduced, the output signal-to-noise ratio is increased, and this method improves the performance of array signal processing. The experimental results show that the output SNRs of two targets with using the imaginary part of covariance matrix are increased by 3.57 dB and 3.149 dB, respectively, and the output SNRs of two targets with using the reconstructed covariance matrix are increased by 7.027 dB and 6.985 dB, respectively. The real-part elimination of covariance matrix method is easy to implemente, and has a definite value for engineering application.%实际的海洋环境是非常复杂的，存在着海洋自噪声、舰船噪声、生物发声等，阵元接收到的噪声信号存在一定的相关性，此时基于传统阵列信号处理的目标方位估计方法的性能将变差，针对这一问题，提出了一种实部消除方法。首先从阵元接收环境噪声的物理机理出发，将圆环阵接收的噪声场分解为对称噪声场和非对称噪声场，并且研究发现对称噪声场只影响数据协方差矩阵的实部。然后通过消除协方差矩阵实部，达到消除对称噪声场的目的，提高信噪比，但是同时产生了虚假声源。针对虚假声源的问题，提出了基于优化算法重构协方差矩阵实部的方法，消除了虚假声源的影响。仿真分析与海试数据处理结果表明：该方法明显消除了对称噪声，提高了信噪比，改善了阵列信号处理算法的性能。实部消除方法易于实现，有一定的工程应用价值。
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