Exploiting Sparsity for Robust Sensor Network Localization in Mixed LOS/NLOS Environments

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.

1. INTRODUCTION

Location awareness plays an important role for a variety of applications, such as, crowd sensing [1], Internet of Things [2] and smart environments [3]. As one promising paradigm, cooperative localization has received much attention due to its many appealing advantages. Existing cooperative localization approaches can be broadly categorized into probabilistic [4]–[7] and deterministic ones [8]–[13]. In general, deterministic approaches are computationally lighter than the probabilistic ones and, thus they are more suitable for localization in wireless sensor networks, which are comprised of light-weighted sensors with limited power and computational capability.