Searching Integer Ambiguities in Single Frequency Single Epoch by Genetic Algorithm

Carrier phase ambiguity solution for single frequency single epoch is the key to high precision for real-time kinematic positioning. An evolution algorithm-Genetic Algorithm was introduced to search GPS carrier phase integer ambiguities in single frequency single epoch. It is a new optimization method through simulating the process of nature evolution to search the best solution. An improved regularization method is applied to improve the accuracy of the float solution of the ambiguities. The improved float solution increases the success rate of fixing the carrier phase integer ambiguities, since not only the candidate integer set is reduced, but also the ambiguity float solution is improved. An example is illustrated to show the new method's effect. The result processed by Bernese GPS software using all GPS data in this example is taken as the standard of comparison. Then, some L 1 phase observations and C/A pseudo ranges are extracted, and each epoch's float solution is computed by the new regularization method. After obtaining more accurate ambiguity float solution, we could think ambiguity true value is around float solution for 3 circles. Under this precondition, Genetic Algorithm was used to search carrier phase integer ambiguity. The results indicated that the new method not only increased the success rate of fixing ambiguity, but also improved the search efficiency. Genetic Algorithm spent less time in searching integer ambiguity than that LAMBDA did. And Genetic algorithm was also proved a robust algorithm by some analyses using GPS data. The new research way based on the improving float solution will make great application significance to real-time kinematic positioning.