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In order to study the performance of different elimination methods on the distance estimation forward error caused by the non-line-of-sight (NLOS) propagation of radio signals, this paper is based on the mean value, root mean square delay spread, skewness, kurtosis and peak-to-average ratio extracted from the channel state information (CSI), and combine it with the logarithmic estimated distance based on the time of arrival (TOA) as the feature input vector, through the establishment of Gaussian process regression (GPR), least square support vector machine regression (LS-SVMR) and BP neural network training model for experimental performance comparison. Through the actual measurement of the 2.4 to 5.4 GHz wireless propagation channel in the typical indoor environment, the error elimination experiment is carried out to compare the NLOS error elimination performance under different input characteristics, different bandwidths and different frequency bands. The experimental results show that the GPR model has the best NLOS error elimination performance, and the extracted CSI multi-features as the input of the GPR model can reduce the average absolute error and root mean square error by 71.12% and 81.36%, respectively. As the bandwidth continues to increase, the error elimination performance is gradually optimized. By increasing the bandwidth, the NLOS positioning error when the input features are less can be effectively improved. The positioning error of the low frequency band is smaller than that of the high frequency band under the multi-features, so the combination of all available frequency bands can eliminate the NLOS positioning error better than a single frequency band.