Robust Convex Approximation Methods for TDOA-Based Localization Under NLOS Conditions
Robust Convex Approximation Methods for TDOA-Based Localization Under NLOS Conditions
复制标题
非视距条件下基于 TDOA 的鲁棒凸逼近方法
DOI：
10.1109/tsp.2016.2539139
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发表时间：
2016-07-01
影响因子：
5.4
通讯作者：
Li, Youming
中科院分区：
文献类型：
--
作者：
Wang, Gang;So, Anthony Man-Cho;Li, Youming
在本文中，我们开发了一种新的鲁棒优化方法，利用在非视距（NLOS）条件下收集的到达时间差（TDOA）测量数据进行源定位。我们的方法的一个关键特征是，它不需要了解NLOS误差的分布或统计数据，这在实践中通常很难获得。相反，它只假设NLOS错误具有有限的支持。基于这一假设，我们将基于tdoa的源定位问题表述为鲁棒最小二乘（RLS）问题，其中寻求对NLOS误差具有鲁棒性的位置估计。由于RLS问题是非凸的，我们提出了两种有效实现的基于凸松弛的近似方法来解决它。然后，我们对这两种方法的近似质量和计算复杂度进行了深入的理论分析。特别是，我们建立了条件，在这些条件下，它们将产生源的独特定位。合成数据和真实数据的仿真结果表明，该方法在各种NLOS设置下的性能非常稳定，明显优于现有的几种非鲁棒方法。
In this paper, we develop a novel robust optimization approach to source localization using time-difference-of-arrival (TDOA) measurements that are collected under non-line-of-sight (NLOS) conditions. A key feature of our approach is that it does not require knowledge of the distribution or statistics of the NLOS errors, which are often difficult to obtain in practice. Instead, it only assumes that the NLOS errors have bounded supports. Based on this assumption, we formulate the TDOA-based source localization problem as a robust least squares (RLS) problem, in which a location estimate that is robust against the NLOS errors is sought. Since the RLS problem is non-convex, we propose two efficiently implementable convex relaxation-based approximation methods to tackle it. We then conduct a thorough theoretical analysis of the approximation quality and computational complexity of these two methods. In particular, we establish conditions under which they will yield a unique localization of the source. Simulation results on both synthetic and real data show that the performance of our approach under various NLOS settings is very stable and is significantly better than that of several existing non-robust approaches.