通过鲁棒模型转换和神经动力学优化缓解NLOS的基于TDOA的定位
TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization
摘要：
研究了非视距（NLOS）传播条件下利用到达时间差（TDOA）测量对信号源进行定位的问题。目前流行的许多基于TDOA定位的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，以往的研究表明，直接操纵的TDOA度量通常会引起复杂的估计。为了克服这些挑战，我们将未知源的起始时间作为优化变量，并对其施加一定的不等式约束，以恢复潜在的到达时间框架，通过$\ell_1$ -范数鲁棒化来减小NLOS误差，最后应用一个基于增广拉格朗日量和投影定理的硬件可实现的神经动力学模型来求解所得到的非凸优化问题不平等的约束。通过大量的仿真验证，该方案可以达到一个很好的平衡之间的定位精度，计算复杂度，先验知识的要求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
