GNSS Positioning Accuracy Enhancement Based on Robust Statistical MM Estimation Theory for Ground Vehicles in Challenging Environments

• Published in: Applied sciences Vol. 8; no. 6; p. 876
• Main Authors: Akram, Muhammad, Liu, Peilin, Wang, Yuze, Qian, Jiuchao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2018
• Subjects: Accuracy; Aeronautics; Confidence intervals; Estimates; Genetic algorithms; Global positioning systems; GNSS navigation; GPS; Hypothesis testing; IRWLS; LSE; MM estimation; multi-GNSS; Noise; outliers; Outliers (statistics); Receivers & amplifiers; Researchers; Signal processing; Smartphones; Unmanned aerial vehicles; urban canyon; Urban environments; New York; United States > US
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app8060876

[...]it is a problematic task to achieve the positioning accuracy and reliability in such environments and it is required to adopt the positioning estimation techniques to prevent the effect of possible wrong or outlier satellite observation on the user position. [...]for user position estimation in urban environments, visible and available satellites are rapidly changing and it is difficult to use the RAIM for satellite/pseudorange selections. Besides the satellite/observation selection techniques, several researchers also focused to improve the estimation algorithms to enhance the positioning accuracy. [...]the positioning process starts with an unknown random initial estimate such as a least-squares estimate and the iterative reweighting scheme can ignore perfectly good measurements resulting to a wrong convergence point with a bad initial estimate. Using hit and trial approach, it is found that it is possible to relate the sample size nsub to CNo such that the nsub=(n+m)−kCNo<throbs . k is the number of observations having CNo smaller than a defined threshold. Because the presence of contaminated observations, the final position estimate X^rb must meet the following condition, ∑l=1n+mμ(rl(X^rb)srb)=min srb is the corresponding scale factor to the robust estimate X^rb .