Abstract 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 l1-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.

中文翻译：

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通过稳健的模型转换和神经动力学优化，基于 TDOA 的定位与 NLOS 缓解

摘要 本文重新讨论了在非视距 (NLOS) 传播下通过到达时间差 (TDOA) 测量定位信号发射源的问题。许多当前流行的基于 TDOA 的定位中的 NLOS 缓解方法倾向于通过凸松弛来解决其优化问题，因此计算效率低下。此外，先前的研究表明，直接操纵 TDOA 指标通常会产生复杂的估计量。为了绕过这些挑战，我们转而通过将未知源起始时间视为优化变量并对其施加某些不等式约束来检索底层到达时间框架，通过 l1 范数稳健化减轻 NLOS 错误，最后应用基于重新定义的增广拉格朗日和投影定理的硬件可实现神经动力学模型来解决由此产生的具有不等式约束的非凸优化问题。通过大量模拟验证，所提出的方案可以在定位精度、计算复杂性和先验知识要求之间取得良好的平衡。