Low Complexity Localization Algorithm Based on NLOS Node Identification Using Minimum Subset for NLOS Environments

The location estimation in sensor networks is of great current interest. A general approach to location estimation is to gather Time-of- Arrival (TOA) measurements from a number of nodes and to estimate a target location. The two major sources of range measurement errors in geolocation techniques are measurement error and Non-Line-of-Sight (NLOS) error. NLOS errors caused by blocking of direct paths have been considered as one of serious issues in the location estimation. Therefore, Iterative Minimum Residual (IMR) method, which identifies NLOS nodes and removes them from the data set used for localization, has been proposed. IMR improves location estimation precision in comparison with the technique that does not identify and remove NLOS nodes. However, IMR needs a lot of calculation to identify NLOS nodes. In this paper, we propose a low complexity localization algorithm based on NLOS node identification using minimum subset for NLOS environments. We evaluate our proposed algorithm by computer simulation. We show that the proposed method achieves almost the same root mean square error (RMSE) as the conventional method with lower complexity.