为了提高传统地形匹配算法的定位精度问题, 提出了一种基于粒子群优化的飞行器地形匹配新算法。该算法以参考导航系统测量位置为中心规划真实位置的搜索范围, 从基准地形图上提取相应的地形高程数据, 然后将粒子群优化算法引入匹配区搜索, 并将得到的地形剖面像元高程参量作为粒子群体中的粒子。在此基础上, 采用归一化积相关函数作为粒子适应度函数, 通过适应度最大度量值来比较基准子图和实时图剖面的相似程度, 最后通过仿真分析, 比较了基于TERCOM和PSO的匹配算法匹配精度和匹配时间。仿真结果表明, 基于PSO算法的匹配效果优于传统TERCOM算法, 匹配时间虽较TERCOM算法略长, 但满足实时性要求。

In order to improve the positioning accuracy of traditional terrain matching algorithm, a new aircraft terrain matching algorithm based on particle swarm optimization was put forward. The search scope of true location was programmed by taking the measured position of referenced navigation system as the center, terrain elevation data was extracted from the referenced topographic map, then the particle swarm optimization algorithm was introduced into the matching area for searching, and the parameter of pixel height of the terrain profile was used as the particle in particle swarm. On this basis of above, correlation function of normalized product was applied as fitness function of particle, then the degree of similarity of the referenced map with profile of real-time image was compared by maximum fitness value, finally through the simulation analysis, comparison of matching accuracy and time based on TERCOM algorithm and PSO algorithm was presented. Simulation results show that the effect of matching based on PSO algorithm is better than traditional TERCOM algorithm, and matching time is slightly longer than TERCOM, but meet the real-time requirement.