基于粒子群优化的DV-Hop定位算法研究

针对无线传感器网络(WSN)定位算法中的经典DV-Hop算法存在较大定位误差的问题,提出一种基于粒子群优化修正平均每跳距离的DV-Hop优化算法.该算法在以下三个方面进行改进:对于每个锚节点平均跳距计算,加入各个锚节点权重;提出主节点定义,网络拓扑结构将被考虑得更加全面,更好地权衡局部和全局特点,以此方法计算节点估计距离;提出中心学习策略,加入逃逸因子,避免粒子陷入局部寻优,最后用改进的粒子群算法代替极大似然估计法定位节点坐标.通过Matlab仿真软件验证,与原始DV-Hop和PSO-DVhop比较,结果分析此算法具有优越性和可行性.

Research on Improved DV-Hop Localization Algorithm Based on Particle Swarm Optimization

A DV-Hop optimization algorithm based on particle swarm optimization (PSO) to modify average-per-hop distance is put forward to solve the problem of large positioning error of the classical DV-Hop algorithm in wireless sensor network (WSN) localization algorithm.The algorithm is improved in the following three aspects.For each anchor node average jump distance calculation,each anchor node weight is added.The definition of the master node is proposed,the network topological structure will be considered more comprehensively and the local and global characteristics are better balanced,the node estimation distance is calculated in this way.The central learning strategy is proposed,the escape factor is added to avoid the local optimization of particles.Finally,the improved particle swarm algorithm is used to instead of the maximum likelihood estimation to locate the node coordinates.Compared with the original DV-Hop and PSO-DVHop,the simulation results show that the proposed algorithm is superior and feasible.