An Iterative Robust Regularization Method for GPS Positioning

作者：Zhang, Xin-Chun^*

来源：Circuits, Systems, and Signal Processing, 2014, 33(9): 2895-2915.

DOI：10.1007/s00034-014-9775-8

• GPS positioning
• Inverse problem
• Regularization method
• Outlier
• Robust estimation

In shaded areas, Global Positioning System (GPS) signals may suffer significant degradation, making the measurements to be contaminated by different kinds of errors. These errors such as outliers arising due to multipath and line-of-sight signals can degrade the accuracy of GPS positioning. Moreover, the poor geometric constellation of satellites that the receiver is tracking may cause inverse problems, resulting in unstable and unreliable GPS positioning solutions. To tackle these problems, an iterative robust regularization method (IRRM) for the GPS positioning is proposed in this article. The IRRM employs the Cholesky factorization method to preclude computing the inverse of matrices, making it numerically stable and be easily implemented in engineering. Also included in this work is the derivation of the sufficient condition for the IRRM. Performance comparisons of the proposed algorithm with the least squares, the Cholesky factorization method for iterative least squares (CM), and the robust estimation are demonstrated via two static positioning problems. The experimental results, presented herein, exhibit the improved performance of the IRRM over conventional algorithms.

• 出版日期2014-9
• 单位电子科技大学