摘要
针对现有重力导航匹配算法的匹配精度、匹配率受惯导初始位置误差影响较大以及实时性较差等不足,提出了一种基于自适应混沌蚁群径向分析的实时重力辅助导航匹配算法,新算法引入改进的连续域蚁群算法进行优化模型求解,通过进行连续域蚁群算法的信息素的自适应调整,同时对蚁群算法的搜索策略、计算参数、局部信息素进行混沌自适应处理,最终达到提高算法搜索效率、匹配率、抗噪性能的效果,实验结果表明,新算法对惯导初始误差不敏感,匹配率高,实时性强。
To solve the problems of basic gravity aided matching algorithm, i.e., time consuming, low precision and inefficiency when the inertial navigation system （INS） has a large initial error, a real algorithm on adaptive chaotic ant colony optimization-RD is proposed by using modified ant colony algorithm in continuous space to approach an optimizing model. The search efficiency, noise immunity and matching probability are improved by adaptively adjusting pheromone, chaos adaptive manage of ant colony algorithm search strategy, parameter and local pheromone. Experiment shows that it is more robust to INS initial error, matching efficient and good real-time calculation.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2014年第8期454-461,共8页
Acta Physica Sinica
关键词
重力辅助导航
混沌
蚁群算法
自适应
gravity aided navigation
chaotic
ant colony algorithm
adaptive
