Evaluation and Application of the GPS Code Observable in Precise Point Positioning

The accuracy of the Global Positioning System (GPS) observable, especially for the code observable, has improved with the development of Global Navigation Satellite System (GNSS) receiver technology. An evaluation of the GPS code observable is presented in this paper, together with a stochastic model for the code and phase observables in Precise Point Positioning (PPP), established using the evaluated results. The results show that the code observables of Leica GNSS receivers are generally better than those of some other brand receivers and the Root Mean Square (RMS) for the code observables of the Leica GRX1200PRO, which includes the multipath effect, reaches 0·71 m, although Coarse/Acquisition (C/A) code observables are tracked. The static positioning of the code observable can reach centimetre level and the convergence time for the JPLM station is just 2·5 hours. The positioning results show that it is difficult to converge the Up direction to the centimetre level, compared with the North and East directions. The results show that static positioning can be correlated with the accumulation characteristic of the error for the code observable, while that that of the kinematic mode can be correlated to the error value. The shortened PPP convergence times verify that the presented stochastic models are effective.

Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation

The increasement of software complexity directly results in the augment of software fault and costs a lot in the process of software development and maintenance. The complex network model is used to study the accumulation and accumulation of faults in complex software as a whole. Then key nodes with high fault probability and powerful fault propagation capability can be found, and the faults can be discovered as soon as possible and the severity of the damage to the system can be reduced effectively. In this paper, the algorithm MFS_AN (mining fault severity of all nodes) is proposed to mine the key nodes from software network. A weighted software network model is built by using functions as nodes, call relationships as edges, and call times as weight. Exploiting recursive method, a fault probability metric FP of a function, is defined according to the fault accumulation characteristic, and a fault propagation capability metric FPC of a function is proposed according to the fault propagation characteristic. Based on the FP and FPC, the fault severity metric FS is put forward to obtain the function nodes with larger fault severity in software network. Experimental results on two real software networks show that the algorithm MFS_AN can discover the key function nodes correctly and effectively.