Mei Xiaojun, Wu Huafeng, Xian Jiangfeng, Chen Bowen, Zhang Hao, Liu Xia
Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China.
Department of Informatics, Linnaeus University, Växjö 351 06, Sweden.
Sensors (Basel). 2019 Jun 16;19(12):2708. doi: 10.3390/s19122708.
As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect-signal shadowing by waves and unintentional or malicious attacks-outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer-Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.
作为海上多维观测的重要手段，海洋传感器网络（OSNs）能够满足大规模、多因素海洋环境下综合信息观测的需求。对于海洋传感器网络而言，准确的位置信息是数据集的基础。然而，由于多径效应（海浪对信号的遮挡）以及无意或恶意攻击，异常测量值频繁且不可避免地出现，这直接降低了定位精度。因此，在存在异常测量值的情况下提高定位精度是海洋传感器网络中亟待解决的关键问题。在这种情况下，本文提出了一种在海洋传感器网络存在异常测量值时使用接收信号强度指示（RSSI）的鲁棒非合作定位算法（RNLA）。我们首先使用对数正态阴影模型结合一阶泰勒级数来构建定位问题。然而，该问题难以求解，尤其是在存在异常测量值的情况下。因此，我们随后使用平方距离和加权最小二乘法（WLS）将定位问题转化为优化问题，尽管其形式是非凸的。为了得到精确解，该问题随后被转化为结合鲁棒函数的广义信赖域子问题（GTRS）。尽管GTRS仍然是一个非凸框架，但可以通过二分法获得其解。为确保全局收敛，将块近似线性（BPL）方法与二分法相结合。此外，我们进行了克拉美罗下界（CRLB）分析以评估RNLA。针对可变参数进行了仿真。数值结果表明，在存在异常测量值的情况下，RNLA的性能优于其他算法，尽管在某些情况下RNLA的计算时间比其他算法略长。
