The Sparsity Rank Singular Value Decomposition (SRSVD) method and Semi-Exact Augmented Lagrange Multiplier (SEALM) algorithm cannot fit the node trajectory of Wireless Sensor Network (WSN) accurately when the sampling rate is small, the sparse noise is large, and the Gaussian noise exists. In order to solve the problems, a novel Regularized Weighted Incomplete Robust Principal Component Analysis (RWIRPCA) method was proposed. Firstly, the Incomplete Robust Principal Component Analysis (IRPCA) was applied to the fitting of node trajectory. Then, on the basis of IRPCA, in order to better describe the low rank and sparsity of matrices, as well as the anti-Gauss noise performance of enhanced model, the low rank matrix and the sparse matrix were weighted respectively. Finally, the F norm of Gaussian noise matrix was used as a regular term and applied to the fitting of node trajectory. The simulation results show that, the fitting effects of IRPCA and RWIRPCA are better than those of SRSVD and SEALM in the case that the sampling rate is small and the sparse noise is large. Especially, the proposed RWIRPCA can still obtain accurate and stable results when both sparse noise and Gaussian noise exist at the same time.