基于禁忌搜索的车联网蒙特卡洛定位算法

在蒙特卡洛定位算法中引入禁忌搜索算法以提高车联网中快速定位的性能。自组织车联网高速移动的车辆和快速变化的网络拓扑结构,使用传统的蒙特卡洛定位算法,不能迅速的收敛位置信息。在滤波阶段引入禁忌搜索算法对传统蒙特卡洛定位算法进行改进, 优化滤波排除可能性较小的位置点,获得近似最优估计位置采样集。仿真结果表明,改进后的算法在样本采集数、计算时间、定位精度等方面有了显著提升,改进后的算法能更好地解决车联网的定位问题。

A Monte Carlo Localization Algorithm Based on Tabu Search in Car Networking

Tabu search algorithm introduced in Monte Carlo localization algorithm to improve the car networking quickly locate performance.Ad-hoc car networking vehicles moving at high speed and network topology rapidly changing,the use of traditional Monte Carlo localization algorithm,can not quickly converge location information.Tabu search algorithm is introduced in the filtering stage of the traditional Monte Carlo localization algorithm to filter optimization and exclude the small possibility points, to obtain approximate optimal sample set of the estimated position.Simulation results show that the improved algorithm in the number of sample collection,computation time,positioning precision,has been significantly improved,the improved algorithm can better solve the positioning of car networking.