声目标探测识别是目前弥补雷达低空探测不足的重要方法。采集到的声信号通常含有大量环境噪声，如何在去除噪声的同时尽可能多地保留有效信号是该领域的一个难点。针对低空战场环境噪声的特点，提出基于改进阈值经验模式分解的去噪方法。首先对信号进行经验模式分解，然后对实际能量大于噪声能量的固有模式函数进行阈值处理，最后将处理后的固有模式函数进行重构得到去噪信号。为了验证该算法的性能，对直升机声信号加入不同信噪比的典型低空环境噪声进行去噪，并与其他去噪方法进行比较，采用信噪比、均方根误差和平滑度指标进行定量分析。仿真结果表明，该算法对大部分低空环境噪声有良好的去除效果。

The detection and recognition of acoustic target is an important method to compensate for the weakness of radar in low-altitude detection. How to eliminate the noise mixed in the acoustic signal and to retain the useful information as much as possible is still a challenging problem.Inspired by the wavelet threshold, and according to the characteristics of low-altitude battlefield ambient noise, we proposed a new denoising method based on threshold Empirical Mode Decomposition (EMD). Firstly, the signal is decomposed by EMD to obtain the Intrinsic Mode Functions (IMFs). Then, threshold processing is made to the IMFs whose actual energy exceeds the estimated energy. Finally, the processed IMFs are reconstructed to obtain the denoised signal. To evaluate the performance of the proposed methoda simulation is performed using helicopter sound signal corrupted with typical low-level ambient noise under different signal-to-noise ratios.The performance is evaluated in terms of SNR, Root Mean Square Error(RMSE), and smoothness index, and a comparison is made with those of the previous denoising methods.The simulation results show that the proposed method is effective in eliminating most low-altitude ambient noise.