Machine learning based GNSS signal classifcation and weighting scheme design in the built environment:a comparative experiment

None-Line-of-Sight(NLOS)signals denote Global Navigation Satellite System(GNSS)signals received indirectly from satellites and could result in unacceptable positioning errors.To meet the high mission-critical transportation and logistics demand,NLOS signals received in the built environment should be detected,corrected,and excluded.This paper proposes a cost-efective NLOS impact mitigation approach using only GNSS receivers.By exploiting more signal Quality Indicators(QIs),such as the standard deviation of pseudorange,Carrier-to-Noise Ratio(C/N0),elevation and azimuth angle,this paper compares machine-learning-based classifcation algorithms to detect and exclude NLOS signals in the pre-processing step.The probability of the presence of NLOS is predicted using regression algorithms.With a pre-defned threshold,the signals can be classifed as Line-of-Sight(LOS)or NLOS.The probability of the occurrence of NLOS is also used for signal subset selection and specifcation of a novel weighting scheme.The novel weighting scheme consists of both C/N0 and elevation angle and NLOS probability.Experimental results show that the best LOS/NLOS classifcation algorithm is the random forest.The best QI set for NLOS classifcation is the frst three QIs mentioned above and the diference of azimuth angle.The classifcation accuracy obtained from this proposed algorithm can reach 93.430%,with 2.810%false positives.The proposed signal classifer and weighting scheme improved the positioning accuracy by 69.000%and 40.700%in the horizontal direction,79.361%and 75.322%in the vertical direction,and 75.963%and 67.824%in the 3D direction.

Enhancing air traffic operational efficiency by reducing network scale

The growing demand for air travel has led to the saturation of air traffic networks.Conventional methods of adding routes to alleviate congestion and reduce delays may not achieve the desired effect and even degrade system performance.In this paper,we explore the application of Braess’s Paradox in the reduction of air traffic networks.This counterintuitive phenomenon shows that adding new connections to a network can actually increase the overall network pressure.This study uses Hidden Markov methods and the Viterbi algorithm to match air traffic flow with routes,a machine learning approach and a mathematical method to construct cost functions for flight time and traffic volume,and finally uses genetic algorithm and the A*algorithm to detect Braess’s Paradox edges.We uses ADS-B data from the busy month of July 2019 for a case study of the air traffic network over the UK airspace.The results show that Braess’s Paradox is also applicable to multi-flight level air route networks.Removing such network edges can improve system performance.In one day’s case,the total flight time of the day’s traffic volume decreased from 11509.24 minutes to 10459.97 minutes.This equates to an average savings of 4.99 minutes of flight time per flight,which is significant in controlling delay performance.