In ultra-wideband indoor positioning, the signal propagates in the non-line-of-sight scene due to the influence of various obstacles in the complex indoor environment, resulting in positioning errors. Aiming at the issue of the influence of nonlineofsight propagation on indoor positioning accuracy, a nonlineofsight recognition method based on improved complete ensemble empirical mode decomposition with ICEEMDAN was proposed. Firstly, the channel impulse response is completely decomposed to obtain the IMF with different scale characteristics. Secondly, the Pearson correlation coefficient method is used to select some IMFs for reconstruction to retain more effective information, and the wavelet transform is used to obtain effective timefrequency characteristics of the reconstructed signal. Finally, the nonlineofsight signal is identified by constructing a convolutional neural network. The experimental data are based on the 802154a UWB model and opensource data set. The experimental results indicate that the average accuracy of the proposed recognition method reaches 98.5%, which is 5.6% higher than that of other algorithms in the simulation data set and 14.3% higher than that in the PDS data set, which verifies the effectiveness of the proposed recognition method.