基于烟花优化粒子群的室内定位算法研究

针对测距式射频识别室内定位算法定位误差较大的问题，提出了一种基于烟花优化粒子群的室内定位算法。该算法分为测距和定位两个阶段，在测距阶段使用到达相位差进行测距并构建待优化的目标函数。在定位阶段对粒子群优化算法进行改进。为了改进粒子群优化算法在迭代过程中容易落入局部极值的问题，引入了烟花优化算法的爆炸、变异、选择操作，并对选择规则进行改进；算法还根据烟花爆炸算子和变异算子对粒子群算法的速度更新公式进行改进。实验结果表明，该算法能够有效实现对目标的定位，定位平均误差为0.2773m，与基于标准粒子群优化算法的室内定位算法相比具有39.61%的性能提升。

Research on indoor positioning algorithm based on fireworks optimized particle swarm

Ranging Radio Frequency Identification indoor positioning algorithm has the problem of large errors in positioning. An indoor positioning algorithm based on Particle Swarm Optimization based on Fireworks was proposed. The algorithm is divided into two stages: ranging and positioning. In the ranging stage, the Phase Difference of Arrival is used to measure the distance and construct the objective function to be optimized. In the positioning stage, the particle swarm optimization algorithm is improved. In order to improve the problem that particle swarm optimization is easy to fall into local extreme value during the iterative process, the explosion, mutation and selection operations of Fireworks Algorithm are introduced, the selection rules are improved; The algorithm also improves the speed update formula of particle swarm algorithm according to the firework explosion operator and mutation operator. The experimental results show that the algorithm can effectively locate the target, and the average error of positioning is 0.2773m, and compared with the indoor positioning algorithm based on the standard particle swarm optimization algorithm, it has a performance improvement of 39.61%.