Name
Title | ||
|---|---|---|
Exploiting Sparsity for Robust Sensor Network Localization in Mixed LOS/NLOS Environments. |
Abstract | ||
|---|---|---|
We address the problem of robust network localization in realistic mixed LOS/NLOS environments. We make use of the fact that the bias of range measurement errors is not only non-negative but also sparse when LOS dominates, which has been long overlooked in the existing literature. To exploit these two properties, we introduce a sparsity-promoting regularization term and relax the resulting optimization problem to a semi-definite programming (SDP) problem. The proposed method admits a neat mathematical formulation and is computationally cheap. Moreover, its global convergence is guaranteed and it achieves good robustness against NLOS measurements. In numerical results, the proposed method outperforms representative state-of-the-art SDP approaches, in terms of both localization accuracy and computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICASSP40776.2020.9054501 | ICASSP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Jin | 1 | 19 | 3.07 |
| Feng Yin | 2 | 2 | 1.38 |
| Michael Fauss | 3 | 1 | 1.38 |
| Michael Muma | 4 | 144 | 19.51 |
| Abdelhak M. Zoubir | 5 | 1036 | 148.03 |