多半径误差修正自适应布谷鸟优化DV-HOP定位算法

针对WSN节点定位中非测距DV-HOP算法存在较大定位误差，提出了一种多通信半径误差修正自适应布谷鸟优化DV-HOP定位算法（MEACS-D）. 采用锚节点多通信半径广播消息，在跳数计数时将跳数小数化，以减小跳数长短不一造成的误差；再用虚拟相交圆几何方法计算1跳内节点与锚节的距离；通过在未知节点选择平均跳距时，加入各个锚节点权重进行计算来减少平均跳距误差；另引入可自适应搜索步长的布谷鸟算法代替极大似然估计法来定位节点坐标，以提高定位精度. 通过仿真，在不同锚节点、通信半径、总节点条件下MEACS-D算法较DV-HOP算法和原布谷鸟DV-HOP算法（CS-D）算法定位误差平均下降39.7%、10.6%，证明MEACS-D算法能有效减少定位误差. 

Adaptive Cuckoo Optimization DV-HOP Localization Algorithm Based on Multi-Communication Radius and Error Correction

An adaptive cuckoo optimization DV-HOP localization algorithm based on multi-communication radius and error correction （MEACS-D）is proposed, aiming at addressing the problem of lager error in wireless sensor networks of range-free DV-HOP algorithms. Using the anchor nodes, which have the ability to broadcast multi-communication radius messages, and to decimalize a counted number of hops （to reduce error caused by different jump lengths）, the virtual intersecting circle geometric method was used to calculate the distance between unknown nodes and anchor nodes in one hop. When the unknown node was used to select the average jump distance, consideration was given to adding each anchor node weight to reduce error. To calculate the node coordinates in order to improve the localization accuracy, the cuckoo algorithm with adaptive search step was introduced instead of the maximum likelihood estimation method. In simulation, by varying the values for the anchor nodes, communication radius, and total node, the localization error of the MEACS-D algorithm was reduced by 39.7% and 10.6%, compared to the DV-HOP algorithm and the original cuckoo DV-HOP （CS-D） algorithm, respectively. These results confirm that the MEACS-D algorithm can reduce the localization error effectively.