Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81174
Title: Intelligent GPS L1 LOS/Multipath/NLOS classifiers based on correlator-, RINEX- and NMEA-level measurements
Authors: Xu, B 
Jia, Q 
Luo, Y
Hsu, LT 
Keywords: Global positioning system (GPS)
Software-defined receiver (SDR)
Signal classification
Non-line-of-sight (NLOS)
Multipath
Support vector machine (SVM)
Urban environment
Issue Date: 8-Aug-2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, 8 Aug. 2019, v. 11, no. 16, 1851, p. 1-23 How to cite?
Journal: Remote sensing 
Abstract: This paper proposes to use a correlator-level global positioning system (GPS) line-of-sight/multipath/non-line-of-sight (LOS/MP/NLOS) signal reception classifier to improve positioning performance in an urban environment. Conventional LOS/MP/NLOS classifiers, referred to as national marine electronics association (NMEA)-level and receiver independent exchange format (RINEX)-level classifiers, are usually performed using attributes extracted from basic observables or measurements such as received signal strength, satellite elevation angle, code pseudorange, etc. The NMEA/RINEX-level classification rate is limited because the complex signal propagation in urban environment is not fully manifested in these end attributes. In this paper, LOS/MP/NLOS features were extracted at the baseband signal processing stage. Multicorrelator is implemented in a GPS software-defined receiver (SDR) and exploited to generate features from the autocorrelation function (ACF). A robust LOS/MP/NLOS classifier using a supervised machine learning algorithm, support vector machine (SVM), is then trained. It is also proposed that the Skymask and code pseudorange double difference observable are used to label the real signal type. Raw GPS intermediate frequency data were collected in urban areas in Hong Kong and were postprocessed using a self-developed SDR, which can easily output correlator-level LOS/MP/NLOS features. The SDR measurements were saved in the file with the format of NMEA and RINEX. A fair comparison among NMEA-, RINEX-, and correlator-level classifiers was then carried out on a common ground. Results show that the correlator-level classifier improves the metric of F1 score by about 25% over the conventional NMEA- and RINEX-level classifiers for testing data collected at different places to that of training data. In addition to this finding, correlator-level classifier is found to be more feasible in practical applications due to its less dependency on surrounding scenarios compared with the NMEA/RINEX-level classifiers.
URI: http://hdl.handle.net/10397/81174
EISSN: 2072-4292
DOI: 10.3390/rs11161851
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Xu, B.; Jia, Q.; Luo, Y.; Hsu, L.-T. Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements. Remote Sens. 2019, 11, 1851, 1-23 is available at https://dx.doi.org/10.3390/rs11161851
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