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A Novel Cycle Slips Detection Model for the High Precision Positioning

Quality of the observations, which is significantly affected by cycle slips, is a key factor affecting the positioning results in the high precision positioning. Traditional observation data error detection algorithms always miss some special data error combinations, which is also called the “blind detection spots” problem. For solving the problem, a complementary symmetric geometry-free (CSGF) method is proposed, which makes the detection of cycle slips more comprehensive and accurate. However, when the observation sampling interval becomes larger, the channel state cannot be seen as a constant which reduces the successful detection probability of the cycle slips. Then, a CSGF second-order differential model is further proposed to deal with this problem. The experimental results show that the proposed model significantly improves the accuracy of cycle slip detection even with long sampling interval. The results also indicate that the accuracy and convergence speed of the positioning solution are significantly improved than other schemes.