一种最优锚节点集合上的两重粒子群优化DV-Hop定位算法

当前粒子群优化的DV-Hop定位改进算法，网络中所有的锚节点都参与优化，但是一部分到未知节点估算距离误差较大的锚节点会引入大的定位误差。针对这种情况，首先提出了最优锚节点集合的概念；然后在定位过程中，应用离散粒子群算法构造了最优锚节点集合；最后在最优锚节点集合上应用连续粒子群算法对定位结果进行了优化。仿真实验表明，最优锚节点集合上的两重粒子群优化DV-Hop算法比DV-Hop和一次粒子群优化的DV-Hop明显提高了定位精度。

A kind of double particle swarm optimization DV-Hop localization algorithm based on best anchor nodes set

For the improved DV-Hop algorithms based on particle swarm optimization,almost all the anchor nodes in the network are involved in the locating optimization. However,the anchor nodes which have obvious errors between anchor and unknown nodes will lead to big errors of localization. Hence,the concept of best anchor nodes set is pro-posed. During the process of location,the best anchor nodes set is constructed by employing the discrete particle swarm optimization algorithm. Then the continuous particle swarm optimization algorithm is used to optimize locali-zation for the best anchor nodes set. The simulation results show that the proposed method is effective.