Conventional reverberation reduction methods are conducted with single-ping data and may fail in a low signal-to-reverberation ratio (SRR) environment. To improve the performance of reverberation reduction, multi-ping data are fully considered in this paper. The reverberation can be treated as a combination of the steady component of reverberation and reverberation fluctuations, and then an alternating direction multiplier method is proposed to reduce the steady component of the reverberation. By exploiting the evolution of the target location along multiple pings, the reverberation fluctuation is reduced by the probabilistic data association method. The proposed method was verified by the field data, and the results show that compared with the accelerated proximal gradient method, the sparse coefficient is improved by a factor of 1.23, and the signal excess is improved by an average value of 2.0 dB. In addition, the performance of the proposed method is found to be closely related to the signal-to-reverberation-fluctuation ratio rather than only the SRR.

中文翻译：

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运动目标场景下基于多ping关联的混响减少

传统的混响降低方法是通过单次ping数据进行的，并且在低信噪比（SRR）环境中可能会失败。为了提高混响降低的性能，本文充分考虑了多声平测数据。可将混响视为混响的稳定分量和混响波动的组合，然后提出一种交替方向乘数法来减少混响的稳定分量。通过利用沿多个ping的目标位置的演变，通过概率数据关联方法减少了混响波动。现场数据验证了该方法的有效性，结果表明，与加速近端梯度法相比，该方法的稀疏系数提高了1.23倍，信号过剩的平均值提高了2.0 dB。另外，发现所提出的方法的性能与信号-混响-波动比率而不是仅与SRR密切相关。