A triple-frequency cycle slip detection and correction method based on modified HMW combinations applied on GPS and BDS
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By taking advantage of the additional combined signals introduced by triple-frequency GNSS, we propose a cycle slip detection and correction method based on the traditional extra-wide-lane Hatch–Melbourne–Wübbena (HMW) combination and also modified HMW combinations. Instead of using the combined code signals directly in the traditional HMW combination, the modified HMW combination adopts the original code signals and one combined phase signal with corrected cycle slips to eliminate the ionospheric bias and reduce the effect of the noise induced by the code measurement. To determine the optimally combined signals and the corresponding coefficients in the modified HMW combination, four constrained conditions are proposed based on the maximum acceptable ionospheric bias and measurement noise of the combination in the process of cycle slip detection. Two optimally combined signals are selected; however, the second best signal cannot maintain a 100% success rate when epoch intervals are increased, due to the effect of the remaining ionospheric bias. To solve this problem, a scale factor is introduced to balance the corrected percentage of the ionospheric bias and the amplification of the measurement noise. These selected signals are further tested with real triple-frequency GPS and BDS observations. Results show that the proposed method can provide a 100% success rate in detecting cycle slips in the observations with large epoch intervals (up to 30 s) from medium earth orbit satellites with elevation angles above 5°, as well as inclined geosynchronous orbit and geostationary orbit satellites with elevation angles above 20°.
KeywordsGPS BDS Triple frequency Cycle slips
The authors gratefully acknowledge Jet Propulsion Laboratory and the Curtin GNSS Research Center for providing GNSS products and data, respectively. This work was carried out at the International Doctoral Innovation Center (IDIC). The authors acknowledge the financial support from Ningbo Education Bureau, Ningbo Science and Technology Bureau, China’s MOST and The University of Nottingham. The work is also partially supported by the Ningbo Science and Technology Bureau as part of the International Academy for the Marine Economy and Technology (IAMET) Project “Structural Health Monitoring of Infrastructure in the Logistics Cycle” (2014A35008), Young Scientist program of Natural Science Foundation of China (NSFC) with a project code 41704024, Zhejiang Provincial Natural Science Foundation of China under Grant no. LY16D040001 and ‘the Open Foundation of Key Laboratory of Precise Engineering and Industry Surveying of National Administration of Surveying, Mapping and Geoinformation’ (PF2017-6). The authors would like to acknowledge Dr. Lingyong Huang, from China Aerospace Surveying and Mapping Satellite Center, Beijing 102102, China, due to his help during the developing of the algorithm.
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