摘要
结合粒子群算法和牛顿迭代法的优点,提出了一种基于粒子群初始值选取和牛顿法精确迭代的目标定位方法。该方法充分发挥粒子群算法的群体搜索性和牛顿法的局部细致搜索性,克服了粒子群算法后期搜索效率低下和牛顿迭代法对初始值敏感的缺陷。仿真结果表明,该方法能有效地提高目标定位的准确性,在随机噪声干扰方差为0.5的条件下,定位均方误差不超过1.7 m。
This paper proposed a hybrid method of target localization.It had well combined their advantages of particle swarm algorithm in the initial value selection and Newton iterated method in the precise iteration.The hybrid algorithm had sufficiently displayed the group searching characteristics of particle swarm algorithm and the local strong searching of Newton iterated method. It effectively overcame the shortcoming of particle swarm algorithm which reduced the searching efficiency in later period and the problem of high sensitivity to initial point of Newton iterated method. The simulation results indicate that it could carry on the localization effectively through adopting the hybrid particle swarm and Newton iterated algorithm.When the variance of random noise interference is 0.5, the localization RMSE was below 1.7 m.
出处
《计算机应用研究》
CSCD
北大核心
2010年第5期1700-1701,1713,共3页
Application Research of Computers
基金
山西省自然科学基金资助项目(2007012003)
电子测试技术国防科技重点实验室基金资助项目(9140C1204040908)
关键词
目标定位
粒子群
牛顿迭代法
时差测量
target localization
particle swarm
Newton iterated algorithm
time difference measurement
