Abstract

Real-time cycle slip detection and repair is one of the key issues in global positioning system (GPS) high precision data processing and application. In particular, when GPS stations are in special environments, such as strong ionospheric disturbance, sea, and high-voltage transmission line interference, cycle slip detection and repair in low elevation GPS observation data are more complicated than those in normal environments. For low elevation GPS undifferenced carrier phase data in different environments, a combined cycle slip detection algorithm is proposed. This method uses the first-order Gauss–Markov stochastic process to model the pseudorange multipath in the wide-lane phase minus narrow-lane pseudorange observation equation, and establishes the state equation of the wide-lane ambiguity with the pseudorange multipath as a parameter, and it uses the Kalman filter for real-time estimation and detects cycle slips based on statistical hypothesis testing with a predicted residual sequence. Meanwhile, considering there are certain correlations among low elevation, observation epoch interval, and ionospheric delay error, a second-order difference geometry-free combination cycle slip test is constructed that takes into account the elevation. By combining the two methods, real-time cycle slip detection for GPS low elevation satellite undifferenced data is achieved. A cycle slip repair method based on spatial search and objective function minimization criterion is further proposed to determine the correct solution of the cycle slips after they are detected. The whole algorithm is experimentally verified using the static and kinematic measured data of low elevation satellites under four different environments: normal condition, high-voltage transmission lines, dynamic condition in the sea, and ionospheric disturbances. The experimental results show that the algorithm can detect and repair cycle slips accurately for low elevation GPS undifferenced data, the difference between the float solution and the true value for the cycle slip does not exceed 0.5 cycle, and the differences obey the normal distribution overall. At the same time, the wide-lane ambiguity and second-order difference GF combination sequence calculated by the algorithm is smoother, which give further evidence that the algorithm for cycle slip detection and repair is feasible and effective, and has the advantage of being immune to the special observation environments.

Highlights

  • High-quality global positioning system (GPS) observation data play a key role in obtaining high precision GPS positioning

  • To analyze and verify the feasibility of the cycle slip detection and repair methods proposed in this paper, the following four different environments and different sampling rates of GPS static and kinematic measured data were used for testing

  • The experimental test results of data with different cycle slip combination detection algorithm proposed in this paper can detect the epoch sampling rates at various low elevation under four special environments show that the positions of small and large cycle slips accurately in real time, and it is feasible for cycle cycle slip combination detection algorithm proposed in this paper can detect the epoch slip detection and easy to implement

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Summary

Introduction

High-quality global positioning system (GPS) observation data play a key role in obtaining high precision GPS positioning. When a satellite signal is blocked by an obstacle and fails to reach a receiver, or the satellite signal experiences temporary loss of lock due to external interference and harsh environment in which the receiver is located, an integer number of cycles in the phase observable jumps suddenly. Cycle slips will lead to destruction of continuous primary carrier phase observation values, which will seriously affect high precision gps data processing results. Accurately detecting and repairing cycle slips is an important data pre-processing task in high precision GPS positioning and applications. There are two approaches for processing cycle slips: one is to detect and repair; and the other is to use cycle slips as an unknown ambiguity parameter, which is estimated together with other unknown parameters in subsequent data processing [5,6]

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