A Convex Optimization Approach For NLOS Error Mitigation in TOA-Based Localization

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A Convex Optimization Approach For NLOS Error Mitigation in TOA-Based Localization

Huafeng Wu; Linian Liang; Xiaojun Mei; Yuanyuan Zhang

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques.

Target localization technology plays an important role in various applications, such as positioning and tracking systems [1][2][3]. In what concerns the localization technology, time-of-arrival (TOA) based techniques are promising compared with received signal strength (RSS) or time-difference-of-arrival (TDOA) and angle-of-arrival (AOA)-based techniques in terms of the accuracy and the cost. Therefore, the TOA-based techniques have been studied extensively [4][5].

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