论文部分内容阅读
针对DV-Distance定位算法受网络拓扑结构和环境噪声影响大等问题,从2个方面对算法进行了改进。改进的DV-Distance算法在原算法基础上,通过设定共线度阈值来优化多跳网络中定位锚节点分组的选择,使其能更好的适应低密度、不规则网络,并利用最小二乘算法与泰勒级数展开法相结合方法来估计最终位置,从而提高算法的鲁棒性。仿真结果表明,改进算法与原算法相比,具有更高的定位精度,并且在锚节点数量比较小和节点密度低情况下,仍具有较好的定位性能。
The DV-Distance localization algorithm is greatly affected by the network topology and environmental noise, and the algorithm is improved from two aspects. Based on the original algorithm, the improved DV-Distance algorithm optimizes the selection of positioning anchor nodes in multi-hop network by setting the threshold of collinearity so that it can better adapt to low density and irregular network, and uses the least square The algorithm combined with Taylor series expansion method to estimate the final position, so as to improve the robustness of the algorithm. The simulation results show that the improved algorithm has higher positioning accuracy than the original algorithm and still has better positioning performance under the condition of smaller number of anchor nodes and lower node density.