船舶区域网络中ELCV三维定位算法研究

Liu Yang, Xia HaiRong, Wu Hua, Wu Xiaoming, Zhong Linghui, Xing Jinaping, Zhang Hui
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引用次数: 0

摘要

我们在之前的文献中提出了一种用于船舶区域网络(SANs)的三维定位算法。在本文中,为了进一步探索电动汽车的性能,我们还进行了一些其他的综合仿真。在ELCV中,引入了一个经典的噪声模型来描述在san中观测到的声学背景噪声。同时,将随机通信范围节点放置在san中。ELCV的目的是在异常值存在的情况下提供未知节点的鲁棒估计。在该算法中,传感器节点也具有随机通信范围,该范围可以在设定的范围内改变。使用ELCV,每个单独的未知节点将从锚点获取数据包,然后在由八个相邻锚点组成的立方体空间中的某个给定点上求解空间节点。与周围其他三个相关锚节点形成对称四面体。最后通过质心算法,在对称四面体上得到精度高、鲁棒性好的节点位置估计。在此工作中,改变了更多的参数,并考虑了不同的环境参数,给出了仿真结果。通过这些仿真结果进一步证明了该算法的准确性、有效性和鲁棒性。同时在MATLAB软件中完成仿真过程。DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2704全文:PDF
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of ELCV 3D Localization Algorithm in Ship Area Networks
We propose a 3D localization algorithm used in ship area networks (SANs) in the literature before. In this paper in order to explore more performances of ELCV we made some other comprehensive simulations. In ELCV a classic noise model is introduced to characterize the acoustic background noise observed in SANs. Meanwhile random communication range nodes are placed in the SANs. ELCV is addressed to provide robust estimation of unknown nodes in the presence of outliers. In this algorithm sensor nodes are also equipped with random communication range that can be changed during a set scope. With ELCV, each individual unknown node will acquire data packages from anchors and then solve for a spatial node on some given point in cube space formed by eight neighbor anchors. With other three related anchor nodes around symmetric tetrahedron can be formed. Finally by centroid algorithm, in this symmetric tetrahedron, estimated node positions with accuracy and robustness are obtained. In this work more parameters are changed and different environment arguments are taken into account and then simulation results are given. By these simulation results we further prove the accuracy, efficiency and robustness in SANs. Meanwhile simulation processes are finished in MATLAB software. DOI:  http://dx.doi.org/10.11591/telkomnika.v11i6.2704 Full Text: PDF
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