Cycle Slip Detection And Correction Of COMPASS Based On Sequential Learning

COMPASS navigation system is the global satellite navigation system researched only by our country, among the four navigation systems in the world including GPS of the USA, GLONASS of Russia and Galileo of the EU. With the development of COMPASS, corresponding techniques and applications are becoming hot topics. COMPASS observes carrier phase in high accuracy positioning systems, which inevitably meets cycle slip. Cycle slip influences the accuracy of ambiguity, which results in a large error. Cycle slip detection and correction is an essential work in high accuracy positioning based on COMPASS. In this paper, a new method of cycle slip detection and correction is proposed which is based on sequential learning.The main work and innovations of this paper lie as follows:Ⅰ. A new method of detection and correction of satellite signals based on sequential learning which uses the OS-ELM (Online-Sequential Learning Machine). Firstly, it gets the high order difference series of carrier phase and reconstructs them in phase space. Then by OS-ELM prediction model, the cycle slips of the high order difference series can be detected and corrected. This method is valid on cycle slips that larger than 3 cycles without additional information, which performs well in both innovation and advancement.Ⅱ. Verified experiments for the proposed method on single COMPASS receiver. It takes carrier phase by single COMPASS receiver, and verifies the effectiveness of the method based on OS-ELM. By changing experiment parameters, it discusses the influence of numbers of neurons and evaluation functions. The characteristics and applications of the proposed method are concluded by comparing with several common methods.