A Non-Line-of-Sight mitigation localization algorithm for sensor networks using clustering analysis

Non-Line-of-Sight propagation of wireless signal has an impact on measured distances in range-based localization and will bias the final localization results. A new localization algorithm is proposed in this paper to mitigate Non-Line-of-Sight errors when there are more than enough anchor nodes deployed around the node to be located. This algorithm utilizes multi-round clustering analysis to filter the pre-located estimators which derive from all possible subsets of measured distances. In each round, the method density-based spatial clustering of applications with noise is adopted. Simulations show that the proposed algorithm can effectively improve localization accuracy not only when the measured distances with Non-Line-of-Sight error are minor but also under the condition that all of them suffer random Non-Line-of-Sight error.