| 基于自适应粒子群算法的目标定位方法研究 |
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| 引用本文： | 姚金杰,韩焱.基于自适应粒子群算法的目标定位方法研究[J].微电子学与计算机,2011,28(1):88-91. |
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| 作者姓名： | 姚金杰 韩焱 |
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| 作者单位： | 中北大学,电子测试技术国家重点实验室,山西,太原,030051 |
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| 基金项目： | 山西省研究生优秀创新项目，电子测试技术国防科技重点实验室基金，中北大学校青年科学基金 |
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| 摘 要： | 针对现有定位求解算法复杂和标准粒子群算法易陷入局部最优的缺点,提出了一种基于自适应粒子群算法的目标定位方法.该方法在迭代过程中指数更新惯性权重,择优选择粒子,并根据种群适应度方差值自适应地调整变异概率的大小,增强算法跳出局部最优的能力.仿真结果表明该方法能有效地提高目标的定位精度,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0.8m.
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| 关 键 词： | 目标定位 粒子群算法 自适应变异 时差测量 |
Research on Target Localization Based on Adaptive Particle Swarm Optimization Algorithm |
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| YAO Jin-jie,HAN Yan.Research on Target Localization Based on Adaptive Particle Swarm Optimization Algorithm[J].Microelectronics & Computer,2011,28(1):88-91. |
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| Authors: | YAO Jin-jie HAN Yan |
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| Affiliation: | YAO Jin-jie,HAN Yan(National Key Laboratory of Electronic Testing Technology,North University of China,Taiyuan 030051,China) |
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| Abstract: | A new method of target localization based on adaptive particle swarm optimization algorithm is proposed in view of the shortcoming of the existing localization algorithm and standard particle swarm optimizer algorithm,which has a complex calculation and is easy to be trapped in local optimum.The method has improved the inertia weight of the standard particle swarm optimizer algorithm,selected the particle swarm,and added adaptive mutation operation in iteration process to enhance its ability to break away f... |
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| Keywords: | target localization particle swarm algorithm adaptive mutation time difference measurement |
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