To address the difficulty of fixing full ambiguity caused by half-cycle ambiguities in dy-namic environments,a stepwise Beidou triple-frequency partial ambiguity resolution method is proposed.Initially,based on the characteristic of half-cycle ambiguity,a continuous observation epochs partial ambiguity resolution(COE-PAR)method is designed to preliminarily select high-quality ambiguity subsets.This method reduces the computational load for subsequent partial am-biguity resolution.Furthermore,a cascading partial ambiguity resolution(CPAR)method is de-signed.This method employs multiple screening methods based on observation quality to sequen-tially fix the ambiguities of extra-wide-lane,wide-lane,and narrow-lane.Then,the partial ambi-guities free from half-cycle ambiguities are fixed.Ultimately,the effectiveness of these methods is validated through dynamic vehicle-mounted experiments.The experimental results demonstrate that the stepwise Beidou triple-frequency partial ambiguity resolution method can effectively en-hance the ambiguity fixing rates for kinematic-to-kinematic relative positioning.In urban dynamic environments,compared to traditional full ambiguity resolution method,the ambiguity fixing rates,validated by Ratio test,are improved from 91％to 100％,and the relative positioning errors are reduced by 18％to 79％.Moreover,the COE-PAR method reduces the execution times of the least squares ambiguity decorrelation adjustment(LAMBDA)method during ambiguity exclusion in the CPAR method by approximately 55％.
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