Abstract
To overcome the strikes from the tremendous noise of pseudorange and the integer ambiguity of carrier phase observation, we developed a novel dual-domain filtering method that integrates the traditional observation- and an innovative position- domain filtering method. The novel method employed the conventional Hatch's smoother to improve the precision of pseudorange at the observation-domain filtering part. At the position- domain filtering part, the proposed method constructs an accurate dynamical model for KF (Kalman filter) or ARKF (adaptive-robust KF) that relies on the TDCP (time differential carrier phase) velocity measurement technology. To assess the effectiveness of the novel method, we compared it with other methods under various system integration by measured data from IGS (International GNSS Service) MGEX (Multi-GNSS Experiment) and dynamic data collected at CUMT (China University of Mining and Technology). The static numerical experiments obtained from IGS datasets show that the ARKF improves the precision of 60/84 and 55/84 situations' directions compared with the LS method based on OP (original pseudorange) and SP (smoothed pseudorange), respectively, and Hatch's smother improves the precision of all situations' directions. The experiment results prove that both types of observation- and position- domain filtering methods can improve positioning precision. Also, the novel dual-domain filtering improved the precision of 80/84 situations' directions compared with the LS method. Moreover, the dynamic experiment shows that the novel method can improve all of the situations' directions except the N (North) direction provided by KF base on SP under GC and U (Upon) direction provided by LS base on SP under GR. The average of improved accuracy is 0.212m, 0.346m, and 0.588m at the E (East), N, U direction and its corresponding average percentage of improved accuracy is 30.2%, 60.2%, and 26.7%, respectively, which proves the novel dual-domain filtering method provided the best performance among the tested algorithms.
Highlights
After GPS (Global Positioning System) and GLONASS (GLObalnaja NAwigazionnaja Sputnikowaja Sistema) providing global services, the BDS (BeiDou Navigation Satellite System) II has achieved comprehensive network in the AsiaPacific region with launching 14 satellites by 2012 and BDS III planned to complete the global networking by 2020 [1]
To improve the positioning accuracy of GNSS and extend its application, we proposed a novel dual-domain filtering method that integrates the traditional observationand an innovative position- domain filtering method in this paper
FILTERING METHOD The observation- and position- domain filters are two categories of filtering methods in GNSS technology, and this section introduces the novel dual-domain filtering method applied in this paper
Summary
After GPS (Global Positioning System) and GLONASS (GLObalnaja NAwigazionnaja Sputnikowaja Sistema) providing global services, the BDS (BeiDou Navigation Satellite System) II has achieved comprehensive network in the AsiaPacific region with launching 14 satellites by 2012 and BDS III planned to complete the global networking by 2020 [1]. Generating new measurements that provide high-precision and non-existent ambiguity is a crucial issue to improve positioning accuracy It can divide the existing filters into observation- and position- domain method [2], where the Hatch’s method is the first proposed. F. Li et al.: Novel Dual-Domain Filtering Method to Improve GNSS Performance Based on a Dynamic Model Constructed by TDCP and widely used in the observation-domain [3]. We introduced the observation, stochastic, and dynamic models firstly, and deduced two types of filtering methods employed by the novel method This manuscript tested the static datasets from IGS (International GNSS Service) organization and dynamical receiver at CUMT (China University of Mining and Technology) under various system integration, such as G (GPS), GC (GPS/BDS), GR(GPS/GLO), and GRC (GPS/GLO/BDS). The error propagation theorem is applied to obtain the stochastic model for the ionospheric-free linear combination observation, which expresses as follows: σI2Frs,j (
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