Improved Position Estimation Using Hybrid TW-TOA and TDOA in Cooperative Networks
Improved Position Estimation Using Hybrid TW-TOA and TDOA in Cooperative Networks
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DOI：
10.1109/tsp.2012.2194705
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发表时间：
2012-07
影响因子：
5.4
通讯作者：
M. R. Gholami;S. Gezici;E. Ström
中科院分区：
文献类型：
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作者：
M. R. Gholami;S. Gezici;E. Ström
研究了一个协作式无线传感器网络中，在节点返回时间未知的情况下，多个目标节点的定位问题。在这种类型的合作网络中，两个不同的参考传感器，即，主节点和次节点，测量双向到达时间（TW-TOA）和到达时间差（TDOA），分别。出于次要节点的作用，我们扩展了目标节点的作用，使它们可以被认为是伪次要节点。通过建模周转时间的滋扰参数，我们推导出一个最大似然估计（MLE），构成了一个困难的全局优化问题，由于其非凸的目标函数。为了避免在解决极大似然估计的缺点，我们线性化的测量使用两种不同的技术，即，非线性处理和一阶泰勒级数，并获得基于未知参数的线性模型。所提出的线性估计器分三步实现。在第一步骤中，获得每个目标节点的粗略位置估计，并通过步骤二和步骤三对其进行细化。为了评估不同方法的性能，我们推导出Crame-Rao下限（CRLB）。仿真结果表明，合作技术提供了相当大的改善定位精度相比，非合作的情况下，特别是在低信噪比。
This paper addresses the problem of positioning multiple target nodes in a cooperative wireless sensor network in the presence of unknown turn-around times. In this type of cooperative networks, two different reference sensors, namely, primary and secondary nodes, measure two-way time-of-arrival (TW-TOA) and time-difference-of-arrival (TDOA), respectively. Motivated by the role of secondary nodes, we extend the role of target nodes such that they can be considered as pseudo secondary nodes. By modeling turn-around times as nuisance parameters, we derive a maximum likelihood estimator (MLE) that poses a difficult global optimization problem due to its nonconvex objective function. To avoid drawbacks in solving the MLE, we linearize the measurements using two different techniques, namely, nonlinear processing and first-order Taylor series, and obtain linear models based on unknown parameters. The proposed linear estimator is implemented in three steps. In the first step, a coarse position estimate is obtained for each target node, and it is refined through steps two and three. To evaluate the performance of different methods, we derive the Cramér-Rao lower bound (CRLB). Simulation results show that the cooperation technique provides considerable improvements in positioning accuracy compared to the noncooperative scenario, especially for low signal-to-noise-ratios.