Aiming at the problems that ground penetrating radar signals are easily interfered by noise in complex underground environments, traditional denoising methods have modal aliasing and strong parameter dependence, a denoising algorithm combining the grey wolf algorithm (GWO), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and improved multi-parameter controlled wavelet threshold is proposed. Firstly, the GWO algorithm is used to optimize the noise amplitude (Nstd) and the number of iterations (NE) in ICEEMDAN to achieve adaptive adjustment of parameters and suppress modal aliasing. Then, an improved multi-parameter controlled wavelet threshold function is designed to selectively filter and reconstruct the decomposed modal components to retain the effective signal characteristics. In the simulation experiment, for the weak target scene with a burial depth of 0.1 meters, under the condition of an input signal-to-noise ratio of 10 dB, the output signal-to-noise ratio of this method is improved to 19.308 2 dB, which is an average improvement of 93.26% over the traditional ICEEMDAN with custom parameters combined with soft and hard threshold denoising algorithm. The waveform correlation coefficient is 0.994 15 and the root mean square error is 0.108 3. In the actual measurement experiment, the processed image is clearer and the hyperbolic characteristics of the target are more obvious. This method can effectively improve the signal quality of ground penetrating radar, and has the characteristics of strong adaptability, anti-aliasing and high engineering applicability, providing reliable technical support for the identification of underground targets.