Name
Abstract | ||
|---|---|---|
Localization based on range measurements may suffer from non-line-of-sight (NLOS) bias, which can significantly degrade the accuracy of localization. In this paper, the time-of-arrival (TOA) based localization problem in NLOS environments is addressed. In particular, we approximately model the hybrid noise formed by measurement noise and NLOS bias errors with a Gaussian distribution, and develop a robust estimator based on maximum likelihood (ML) which can mitigate the NLOS bias errors while estimating the location of the source. The Lagrange programming neural network (LPNN) is then applied to address the obtained nonlinear constrained optimization problem. Furthermore, a weighted version of the proposed algorithm is developed by incorporating the distances as weight factors in the formulation. Simulation results show that the proposed algorithms can provide better results as compared with several the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86137-7_7 | WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2021, PT III |
Keywords | DocType | Volume |
Time-of-arrival (TOA), Non-line-of-sight (NLOS), Maximum likelihood (ML) estimation, Lagrange programming neural network (LPNN) | Conference | 12939 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Dong | 1 | 6 | 3.19 |
| Xiaoqing Luo | 2 | 0 | 0.68 |
| GUAN Jian | 3 | 47 | 15.77 |