Fast and Reliable Network RTK Positioning Based on Multi-Frequency Sequential Ambiguity Resolution under Significant Atmospheric Biases

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 13; p. 2320
• Main Authors: Liu, Hao, Zhang, Ziteng, Sheng, Chuanzhen, Yu, Baoguo, Gao, Wang, Meng, Xiaolin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Accuracy; Algorithms; Ambiguity; Ambiguity resolution (mathematics); Artificial satellites in navigation; atmospheric biases; Atmospheric conditions; Bias; Calibration; Code Division Multiple Access; Comparative analysis; Experiments; fast and reliable positioning; Global navigation satellite system; Ionosphere; Kalman filters; Kinematics; Methods; Modelling; Modernization; network real-time kinematic (NRTK); Real time; Reliability; Satellites; sequential ambiguity resolution; Signal processing; Success; Technology application; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16132320

The positioning performance of the Global Navigation Satellite System (GNSS) network real-time kinematic (NRTK) depends on regional atmospheric error modeling. Under normal atmospheric conditions, NRTK positioning provides high accuracy and rapid initialization. However, fluctuations in atmospheric conditions can lead to poor atmospheric error modeling, resulting in significant atmospheric biases that affect the positioning accuracy, initialization speed, and reliability of NRTK positioning. Consequently, this decreases the efficiency of NRTK operations. In response to these challenges, this paper proposes a fast and reliable NRTK positioning method based on sequential ambiguity resolution (SAR) of multi-frequency combined observations. This method processes observations from extra-wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) measurements; performs sequential AR using the LAMBDA algorithm; and subsequently constrains other parameters using fixed ambiguities. Ultimately, this method achieves high precision, rapid initialization, and reliable positioning. Experimental analysis was conducted using Continuous Operating Reference Station (CORS) data, with baseline lengths ranging from 88 km to 110 km. The results showed that the proposed algorithm offers positioning accuracy comparable to conventional algorithms in conventional NRTK positioning and has higher fixed rate and positioning accuracy in single-epoch positioning. On two datasets, the proposed algorithm demonstrated over 30% improvement in time to first fix (TTFF) compared to conventional algorithms. It provides higher precision in suboptimal positioning solutions when conventional NRTK algorithms fail to achieve fixed solutions during the initialization phase. These experiments highlight the advantages of the proposed algorithm in terms of initialization speed and positioning reliability.