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为了充分利用锚节点之间的信息以及提高基于跳数的定位算法精度,提出基于线性回归的两种算法LRDH(Linear Regression DV-HOP)和MMLR(Min and Max Linear Regression).该算法利用线性回归分析的方法,对锚节之间跳数和距离信息建立线性回归模型来计算未知节点与锚节点之间的距离,并将计算结果运用到全网的定位中.仿真结果表明,LRDH算法和MMLR算法定位精度都优于DV-HOP算法,特别是MMLR算法比DV-HOP算法定位精度有大幅的提高.
In order to make full use of the information between anchor nodes and to improve the precision of positioning algorithm based on hops, two algorithms based on linear regression, LRDH (Linear Regression DV-HOP) and MMLR (Min and Max Linear Regression), are proposed.This algorithm uses linear regression , A linear regression model is established between the number of hops and the distance between anchor nodes to calculate the distance between the unknown nodes and the anchor nodes and apply the results to the whole network.The simulation results show that the LRDH algorithm and MMLR The accuracy of the algorithm is better than that of DV-HOP algorithm, especially the MMLR algorithm has a significant improvement in positioning accuracy compared with DV-HOP algorithm.