Xiaopeng Hou, Kun Fang, Beihang University, China; Zhiqiang Dan, Xiao Li, Beijing Hualong Tong Science & Technology Co.,Ltd, China; Kai Guo, Zhipeng Wang, Beihang University, China; Yanbo Zhu, Aviation Data Communication Corporation, China

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When the Global Navigation Satellite System (GNSS) uses high-precision carrier phase measurements for differential positioning, it usually needs to fix the carrier phase ambiguity to improve the accuracy of navigation and positioning. Integer ambiguity resolution is the process of estimating the unknown ambiguity of the carrier phase observable as an integer. At present, the algorithm for ambiguity repair is relatively mature. The integer least square algorithm is mainly used to repair the ambiguity. However, in the process of ambiguity repair, it is necessary to infer the reliability of the fixed solution, because incorrect fixed ambiguity solutions usually lead to unacceptable positioning errors, therefore it is important to be able to evaluate the probability of correct integer ambiguity estimation. The success rate of this blur repair depends on the basic mathematical model and the used integer estimation method. Based on the simulated and measured data, this paper uses different integer estimation methods to evaluate the success rate of ambiguity repair. The approximate value of the ambiguity repair success rate and the calculation method of the boundary are provided. In view of the accuracy of the fixed ambiguity, an algorithm based on the Bayesian posterior probability is proposed to evaluate the success rate of fixed ambiguities. It is assumed that the posterior probability of the parameter estimation is higher than the given confidence level. The Bayesian posterior probability of fixed ambiguity is derived under the strict Bayesian theoretical framework. In dynamic positioning scenarios, the threshold is defined by combining ratio tests and Bayesian posterior probabilities. The partial ambiguity is fixed based on the covariance of ambiguity.