基于区域划分自适应粒子群优化的超短基线定位算法

为了降低由声速不确定引起的水下声学定位误差,提出一种基于区域划分自适应粒子群优化的超短基线定位算法.该算法将声速作为未知量,利用冗余的定位信息构建定位模型,针对标准粒子群算法收敛速度慢及容易早熟的问题,采用区域划分的方法动态调整粒子的惯性权重和学习因子,达到寻优能力与收敛速度的平衡,并引入自适应变异操作避免种群陷入局部最优解.仿真实验表明,所提出的算法能够有效提高声速未知情况下超短基线系统的定位精度.

Ultra-short baseline positioning algorithm based on region-division adaptive particle swarm optimization

An ultra-short baseline positioning algorithm based on region-division adaptive particle swarm optimization is presented in order to reduce the underwater acoustic positioning error caused by inaccurate sound speed. This algorithm regards sound speed as variables and utilizes redundant information to build the positioning model. For the shortcoming that the standard particle swarm algorithm is slow in convergence speed and easy to fall into local optimum, the region-division method is utilized to dynamically adjust the inertia weight and learning factors of each particle to reach the balance between optimization ability and convergence speed, and the adaptive mutation operation is introduced to avoid the population falling into local optimum. Finally, simulation results show that the proposed algorithm can effectively improve the positioning accuracy of the ultra-short baseline system with unknown sound speed.