Model-based processing in sensor arrays
Advances in spectrum analysis and array processing (vol. III)
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Improvement of Location Accuracy by Adding Nodes to Ad-Hoc Networks
Wireless Personal Communications: An International Journal
IEEE Transactions on Wireless Communications
Robust maximum likelihood acoustic source localization in wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Bounds on the number of identifiable outliers in source localization by linear programming
IEEE Transactions on Signal Processing
A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques
IEEE Communications Surveys & Tutorials
| Hi-index | 0.08 |
In this work we examine new ways to solve a time-difference-of-arrival (TDOA) localization problem when the set of measurements is contaminated by outliers. The proposed method relies on the minimization of an L"p-norm based cost function with p@?(0,1]. This norm is known to provide robustness against outliers. Some known positioning method can eventually successfully locate an emitter in the presence of outlier measurements, but it is at the expense of huge computational costs due to multi-dimensional grid search. We propose in this paper a way to dramatically lighten the computational load by reducing the problem to a few linear searches. Even if 70% of the measurements are outliers, the proposed positioning method provides high accuracy location estimates, while keeping the computational load very low. Optionally, the location estimates can be used to identify and reject outliers from the data set, which can then serve as an input of any common TDOA positioning method to obtain refined location estimates. Numerical examples corroborate our results, both in terms of accuracy and of computational time.