Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

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.The former, like the conventional GNSS positioning algorithm, still makes use of pseudorange measurements, meanwhile it is aided with the information of satellite visibility or additional path delay of reflected signals obtained using 3D city models. For shadow matching, the basic idea is to compare the measured signal availability and strength with predictions obtained using 3D city models over a range of candidate positions. For detailed implementation of 3DMA GNSS positioning, readers are referred to [5][6][7][8][9][10]. For the 3DMA GNSS positioning, the accuracy of line-of-sight/non-line-of-sight (LOS/NLOS) signal reception classification directly affects its performance [5]. In addition to the 3DMA GNSS positioning, conventional ranging-based least-squares GNSS positioning can also benefit from an accurate signal classification by excluding or down-weighting the identified multipath (MP)/NLOS measurements [11].Various approaches to classifying LOS/NLOS signal have been proposed. As mentioned above, the 3D ...

Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis

“…Unlike using (13) to assess the performance of NLOS-induced pseudorange measurement error detection, in this test, we adopted the metric of code pseudorange double difference (DD) observable shown in Fig. 24, which can be considered as the pseudorange error mainly caused by NLOS or multipath effects [44]. The definition and calculation of DD can be found in [44].…”

“…24, which can be considered as the pseudorange error mainly caused by NLOS or multipath effects [44]. The definition and calculation of DD can be found in [44]. In this paper, the reference station was chosen as the HKSC station of SatRef, which is established by the Hong Kong land department for providing differential corrections.…”

A Review of Environmental Context Detection for Navigation Based on Multiple Sensors

“…Xu et al [63] also tried to classify GNSS signals into three different categories, LOS, NLOS, and multipath, using to an SVM model. They compared three different classifiers.…”

Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System

“…They attempted to reduce the operating cost of traditional methods and improve usability by constructing a mapping relationship between multiple feature parameters and GNSS signal categories, and achieved good results. In terms of classification algorithms, decision tree [11], support vector machine (SVM) [12,13], convolutional neural network (CNN) [14], and adaptive neural fuzzy inference system (ANFIS) [15] have been successively used for NLOS/multipath recognition. The feature values in most of the above studies are directly or indirectly extracted from the receiver independent exchange (RINEX) format files, which are easy to obtain for GNSS devices.…”

“…The feature values in most of the above studies are directly or indirectly extracted from the receiver independent exchange (RINEX) format files, which are easy to obtain for GNSS devices. The signal-level feature values are used in [13].…”