Research On Quality Control Of RTK In Dynamic Environments And The Problem Of Wild Value Points

Dynamic RTK technology has been widely used in mapping,navigation,disaster deformation monitoring and other fields,in the case of short baselines using the carrier phase through the form of differential can eliminate most of the errors,positioning accuracy can reach centimetre or even millimetre level.However,in dynamic environments,there are often problems such as model errors caused by uncertainty in carrier motion,positioning deviations caused by noisy environmental interference,and lack of visible satellites due to obstruction blockage.This paper addresses such problems by introducing a combined GPS/BDS dual-frequency positioning model and conducting a quality control study,which mainly includes the error compensation of the motion model in the parameter estimation algorithm,the noise-resistant fitting,and the study of the correlation reduction algorithm for the integer ambiguity.The innovations of the thesis are as follows.(1)In the area of error compensation of mathematical models,the influence of mathematical models on parameter estimation is first introduced and the problem of errors caused by inconsistencies between mathematical models and the actual system is investigated.Two main aspects are discussed,one is the proposed introduction of the dynamical model into the dynamic parameter estimation algorithm,and the other is the use of observation and state errors for multi-calendar element fitting,fitting the model error by weighting or averaging,and then updating the Kalman gain,and the effectiveness of the method is verified by actual lap running experiments.(2)In terms of noise-resistant fitting,the possible noise disturbances in real situations and the effects brought about by noise disturbances are analysed.The least squares estimation method is proposed to fit the noise model parameters,while the robust optimal M-estimation estimation algorithm is introduced to predict the model parameters,and finally the corrected observations and state estimates are used to achieve the robust fitting parameter estimation of noise.(3)In terms of ambiguity resolution,an improved double Cholesky lower triangular decomposition algorithm is proposed to address the situation that the integer Gaussian lower correlation algorithm within the framework of LAMBDA algorithm is prone to incomplete lower correlation leading to fixation failure in the high dimensional case,by means of preestimation to perform pre-ordering before lower triangular decomposition,and to arrange the conditional variances in descending order after decomposition so as to improve the overall degree of lower correlation.The lowering correlation experiments were carried out in different dimensions with the integer Gaussian lowering correlation algorithm,the joint lowering correlation algorithm and the double Cholesky lowering triangular decomposition algorithm,and the lowering correlation coefficient was used as an indicator for evaluation.The experimental results show that the improved double Cholesky lower triangular decomposition algorithm has the highest degree of descending correlation,followed by the integer Gaussian algorithm.In the case of high dimensionality,the integer Gaussian algorithm has a reduced correlation coefficient of about 0.65,while the improved algorithm has a stable reduced correlation coefficient of about 0.66,which is slightly better than the integer Gaussian algorithm.And as the dimensionality rises,the improved algorithm’s descending correlation effect is better.It was then applied to a practical test and a comparison experiment was conducted using the integer ambiguity search time after reducing the extent of their relevance as a metric.The actual results showed that the improved algorithm slightly outperformed the integer Gaussian algorithm and improved in the integer ambiguity fixation rate.