Designated to solve the problem of large positioning error caused by large ranging errors in the non-line-of-sight (UWB) environment, a tracking error based on Least Squares Support Vector Machine (LS_SVM) is proposed. The method divides the indoor area into several equal small areas, establishes the non-linear relationship between the eigenvalues of the sampled signals and the node locations in each area, classifies and regulates them by using LS_SVM. For non-line-of-sight distance measurement results, a smaller weight is given. Experimental results show that the error of K-Nearest Neighbors (K-NN) is improved by 10% within 7 cm, which shows that this algorithm can effectively improve the positioning accuracy.