基于多元Taylor级数和AFS的混合定位算法

针对传统Taylor级数定位算法存在精度严重依赖初始值,导致定位精确度不高的缺陷,结合人工鱼群算法和多元Taylor级数展开算法的优点,提出了一种基于人工鱼群算法初值选取与多元Taylor级数展开算法精确求解的混合定位方法。算法充分发挥了人工鱼群算法初值估计性能良好和多元Taylor级数展开算法求解精度高的优点。仿真结果表明:上述算法减少了鱼群数目和迭代次数的选取对定位精度的影响,混合定位算法的精度更高。

Hybrid Localization Algorithm Based on Multivariable Taylor Series and Artificial Fish Swarm

The accuracy of the traditional localization algorithm depends on the initial value seriously, so the positioning accuracy is not high. By combining the advantages of multivariable Taylor series algorithm and the artificial fish swarm algorithm, the paper proposes a localization method based on the artificial fish swarm algorithm of choosing initial values and multivariable Taylor series expansion method of precision solution. The algorithm combines the advantages of good performance in estimation of the artificial fish swarm algorithm and high precision solution of the multivariable Taylor series algorithm. Experimental results indicate that the hybrid algorithm can improve positioning accuracy and reduce the selection of the fish number parameter and iteration number parameter influence on positioning accuracy.