论文标题
通过强大的模型转换和神经动力优化，基于TDOA的本地化，通过NLOS缓解NLOS的本地化
TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization
论文作者
论文摘要
本文重新讨论了在非视线（NLOS）传播下，通过到达时间差（TDOA）测量的时间差异来定位信号源的问题。许多目前在基于TDOA的本地化中NLOS缓解的时尚方法倾向于通过凸放松解决其优化问题，因此在计算上效率低下。此外，先前的研究表明，直接在TDOA度量上进行操作通常会产生复杂的估计器。为了绕过这些挑战，我们通过将未知的源发作时间视为优化变量，并在其上施加某些不平等限制，从而通过$ \ ell_1 $ norm robusticification和最终应用障碍的神经模型，通过$ \ ell_1 $ norm and protedrian proteder proteder proteder proteder proteder protedor proteder，我们转向绕过这些挑战，并在其上施加了一定的不平等限制，并在其上施加了一定的不平等限制。最终的非convex优化问题与不等式约束。通过广泛的模拟来验证，所提出的方案可以在本地化准确性，计算复杂性和先验知识要求之间取得良好的平衡。
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.
