Abstract
The Global Navigation Satellite System (GNSS) precise positioning has drawn increasing attention owing to the growing demand for accurate relative tracking of devices. The carrier phase becomes the most precise measurement available, the solution of carrier phase integer ambiguity is essential for achieving precise GNSS tracking. Methods of searching within the position domain show their advantage over the methods supported ambiguity fixing, e.g., far fewer epochs taken for obtaining the precise solution and proof against to cycle slips. However, the drawbacks of low computation efficiency and also the existence of multi-peak candidates restrict these methods to be utilized in modern GNSS tracking techniques. The novel tracking approach derived in this paper is based on the Segmented Simulated Annealing Modified Ambiguity Function Approach (SSA-MAFA) and the Relative Motion Tracking Method (RMTM). It focuses on reducing the computation time, which is the main drawback of the traditional Ambiguity Function Method (AFM) and giving out the precise relative tracking result efficiently without solving the integer ambiguity fixing problem. We use the SSA-MAFA search method to reduce the computation time, the Kernel Density Estimation (KDE) method to filter out the false peak candidates, and the RMTM method to obtain the precise relative motion vector between two adjacent epochs. Both static and kinematic experiments were carried out to evaluate the performance of the new approach. The static test shows that the RMTM method can give out a millimeter level of accuracy relative motion solution. The kinematic experiment shows that the precise tracking result can be obtained after handling only two epochs of data. Meanwhile, the tracking result of the proposed approach can be a centimeter-level of accuracy, even if the prior position is several meters far from the referenced value.
Highlights
Nowadays, the need for obtaining a precise relative position is becoming more popular, especially in outdoor applications such as collision avoidance, self-driving cars, land surveying, structural health monitoring, and accurate agriculture [1]
The second category of algorithms that search in the position domain shows their advantages over the least-squares ambiguity decorrelation adjustment (LAMBDA) method, such as immune to the cycle slips, far few epochs taken for obtaining the precise solution, and no precise initial position is required
We used the relative motion vector, and the Kernel Density Estimation (KDE) method to filter out the false candidate points, which exist in the search result with utilizing the Segmented Simulated Annealing (SSA)-Modified Ambiguity Function Approach (MAFA) method
Summary
The need for obtaining a precise relative position is becoming more popular, especially in outdoor applications such as collision avoidance, self-driving cars, land surveying, structural health monitoring, and accurate agriculture [1]. The second category of algorithms that search in the position domain shows their advantages over the LAMBDA method, such as immune to the cycle slips, far few epochs taken for obtaining the precise solution, and no precise initial position is required. In [25], the authors presented a novel search method based on the MAFA, and Segmented Simulate Annealing Method called SSA-MAFA It can give out the precise position even if the initial coordinates are meters away from the actual position. The tracking result can be given out without solving the ambiguity fixing problem These methods are only a decimeter or meter level of accuracy, which is less accurate than the solutions by using the AFM class methods. The novel method is based on the Segmented Simulated Annealing Modified Ambiguity Function Approach (SSA-MAFA) and the Relative Motion Tracking Method (RMTM).
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