Zhang Ning’s scientific contributions

The fast ambiguity resolution method, especially that using single epoch single frequency data, has been a challenging issue in the kinematic GPS positioning. In this paper, artificial immune algorithm (AIA) was introduced to search GPS carrier phase integer ambiguities. It is a new optimization method through simulating biological immune systems to search the best solution. AIA can succeed in improving the poor efficiency and stability of the genetic algorithm (GA). If the normal equation is consisted only by single epoch carrier phase, it is always rank deficient. The general method is to combine the C/A code and the L1 carrier phase together, so as to eliminate rank deficiency. In this situation, the normal equation is ill-conditioned. In addition, the pseudo-range is often influenced by high noise, which would result in lower reliability of the float ambiguity solutions. So, the efficiency of kinematic positioning is influenced because the differences are large between the float ambiguity solution and the correct integer ambiguity. To improve the float solution, an unbiased estimation method named Iteration by Modifying Normal Equation (IMNE) is applied. IMNE can efficiently improve the ill-condition of normal equation. By using it, more precise ambiguity float solution can be acquired and the search space of ambiguity can be greatly reduced. The improved float solution increases the success rate of fixing the carrier phase integer ambiguities. After obtaining more accurate ambiguity float solution, AIA was used to search GPS carrier phase integer ambiguity. The results of some examples show that the new approach (INME-AIA) based on the improving float solution is efficient.