【摘要】 针对无线传感网中的节点定位问题，采用RSSI测距技术测量未知节点与信标节点间的距离，并采用粒子群算法进行优化。针对粒子群算法的不足，首先通过改进粒子群算法中的惯性权重和学习因子来提高算法的性能，并引入自适应罚函数解决粒子收敛过快的问题；其次采用计算测量距离误差修正和定位约束模型来优化节点定位的效果。通过仿真实验将所提定位算法与其他算法进行比较，结果表明所提算法在算法的收敛性能和定位精度上取得了比较好的效果，改善了节点的定位效果。更多还原

【Abstract】 In allusion to the difficulty of node positioning in wireless sensor network, the RSSI measuring technology is adopted to measure the distance between unknown node and beacon node, and the particle swarm optimization(PSO) is used for optimization. In order to compensate the deficiency of the PSO, the inertia weight and learning factor in the improved PSO are utilized to improve the performance of the algorithm, the self-adaptive penalty function is introduced to solve the problem of fast convergence of particles, and then the distance error modification and positioning constraint model are used to optimize the effect of node optimization. The proposed positioning algorithm was compared with other algorithms in the simulation experiment. The result shows that the proposed algorithm has achieved better effects on the convergence performance and positioning accuracy,improving the positioning effect of the node.更多还原