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Register a new userTDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization
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Added by
Wenxin Xiong
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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This paper revisits the problem of locating a signal-emitting source from time-difference-of-arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently fashionable methods for NLOS mitigation in TDOA-based localization tend to solve their optimization problems by means of convex relaxation and, thus, are computationally inefficient. Besides, previous studies show that manipulating directly on the TDOA metric usually gives rise to intricate estimators. Aiming at bypassing these challenges, we turn to retrieve the underlying time-of-arrival framework by treating the unknown source onset time as an optimization variable and imposing certain inequality constraints on it, mitigate the NLOS errors through the $ell_1$-norm robustification, and finally apply a hardware realizable neurodynamic model based on the redefined augmented Lagrangian and projection theorem to solve the resultant nonconvex optimization problem with inequality constraints. It is validated through extensive simulations that the proposed scheme can strike a nice balance between localization accuracy, computational complexity, and prior knowledge requirement.
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This paper considers the problem of time-difference-of-arrival (TDOA) source localization using possibly unreliable data collected by the Internet of Things (IoT) sensors in the error-prone environments. The Welsch loss function is integrated into a hardware realizable projection-type neural network (PNN) model, in order to enhance the robustness of location estimator to the erroneous measurements. For statistical efficiency, the formulation here is derived upon the underlying time-of-arrival composition via joint estimation of the source position and onset time, instead of the TDOA counterpart generated in the postprocessing of sensor-collected timestamps. The local stability conditions and implementation complexity of the proposed PNN model are also analyzed in detail. Simulation investigations demonstrate that our neurodynamic TDOA localization solution is capable of outperforming several existing schemes in terms of localization accuracy and computational efficiency.
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