Abstract
In this paper, a non-parametric approach of a chi-square test is proposed to solve the NLOS identification problem. Furthermore, if we know the distribution of the NLOS error, two methods are proposed to estimate the true distance, one is the combination of the non-parametric probability density estimation technique and the Kullback-Leibler (KL) distance, the other is the maximum likelihood (ML) estimation. Some conclusions are got, if the NLOS error is the Gaussian distribution, the above two methods are equivalent, if the NLOS error is uniform, the former method is feasible, the latter method is infeasible, if the NLOS error is exponential, the former method is infeasible, the latter method is feasible. Simulation results and theoretical derivation illustrate that the proposed method is able to estimate the true distance with high accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
