Refinement Of GNSS Stochastic Model With Phase Locking Detector

GNSS signals are susceptible to external environmental factors,which can cause signal weakening or temporary lossing,lowering the quality of GNSS observations.Identifying anomalous observations and establishing an accurate stochastic model is one of the keys factors to achieving high precision positioning.The most common stochastic model-s based on observation quality evaluation indexes such as elevation and signal to noise ratio(SNR)are insufficient to meet the current high precision requirements in complex environments such as shaded,buildings,and glass curtain walls.The Phase Lock Detector(PLD),which can be used to indicate the signal tracking state and evaluate the tracking stability of the carrier phase,is used to evaluate the quality of GNSS observation in this paper,and the relationship between PLD and existing data quality evaluation indicators such as elevation and SNR is deeply analyzed.In view of the shortcomings of existing stochastic models,the stochastic model of GNSS observation with PLD is studied..The following are the main research work and accomplishments.(1)The relationship between PLD,SNR,elevation cycle slip and the double difference residual of carrier phase is thoroughly examined,and the feasibility of PLD as an observation quality evaluation indicator is confirmed.After examining the relationship between the indicators,it is discovered that in the open environment,PLD,SNR and elevation are strongly correlated,however,in the complex environment,PLD and SNR are strongly correlated,but there is no obvious correlation between them and elevation.The periodic variation part and overall variation trend of the carrier-phase double-difference residual and the SNR are essentially the same in a complex environment,but there is a time delay between them.PLD and carrier-phase double-difference residual have a partially correlated periodic variation with no delay,but PLD cannot reflect the general trend of carrier phase double difference residual.there is no significant relationship between elevation and carrier phase double residuals.In the above environments,neither SNR nor elevation can effectively indicate cycle slip,Only PLD has a strong correlation with the likelihood of a cycle slip.(2)A PLD-based GNSS stochastic model is developed and compared with the elevation stochastic model and SNR stochastic model.To begin,by examining the relationship between PLD and carrier phase observation precision,it is discovered that the precision of carrier-phase observation decreases as PLD increases.Then,the curve fitting method is used to create a stochastic model based on PLD.Finally,GNSS observation data from the various environments were used to test and analyze the stochastic model’s positioning performance.The experimental results show that the elevation stochastic model has a lower fixed solution ratio than the PLD stochastic model and the SNR stochastic model.In terms of positioning result reliability and accuracy,the SNR stochastic model is the best overall,but the PLD stochastic model is better in the building environment,and the elevation stochastic model is better than the PLD and SNR stochastic models in the open environment and shade environment.It is clear that there are some flaws in using a single observation quality evaluation index to assess observation quality and determine observation’s weight.(3)A comprehensive stochastic model with PLD is proposed,which improves the accuracy and reliability of GNSS positioning results in complex environments.Given the correlation and complementarity of the three indexes of elevation,SNR and PLD,the SNR template function and the SNR precision template function based on elevation are established.based on the SNR,there is also a PLD template function and a PLD precision template function.The above template function was then used to assess each indicator’s accuracy in indicating the quality of the observation,and the weight of each indicator was determined using principal component analysis.yielding a kind of pre-synthetical stochastic model that takes into account PLD,SNR,and elevation.Simultaneously,because the prior comprehensive stochastic model cannot completely indicate the quality of the observation,and there are differences in the accuracy of the observation between different satellite systems,the robust estimation method is used to detect the gross error,and the Helmert variance component estimation method is used to unified the weight of the observation between different satellite systems.Finally,the positioning performance of the comprehensive stochastic model is analyzed and studied by using GNSS static observation data from various environments.The experimental results show that the comprehensive stochastic model are consistent with those of the elevation-PLD-SNRrobust stochastic model.When compared to stochastic models based on one,two,or three indexes,the fixed solution ratio and the reliability and accuracy of positioning results are improved effectively.In conclusion,the Helmert variance component estimation method has little influence on improving location results,and high accuracy and high reliability of positioning results can be obtained in complex environments by using elevation-PLD-SNR-robust stochastic model.