摘要
论述了目标跟踪的原理和数据融合技术,为了解决移动机器人系统中的传感器存在大量不确定性问题,提出了一种交叉传感器交叉特征(CSCM)数据融合算法,这种算法基于粒子滤波技术,融合多个传感器的信息,合并不同的状态空间模型,以此减弱系统和测量噪声,来估计移动机器人的位置和角度。在仿真实验中,我们分别比较了单一传感器和多传感器数据融合的不同情况,结果表明了这种算法的有效性,并展现了良好的跟踪性能。
The principle of target tracking and data fusion techniques were discussed. To resolve enormous uncertainty that exists in sensors of mobile robots, the Cross-Sensor and Cross-Modality（CSCM） data fusion algorithm was proposed. The algorithm is based on particle filter techniques, fuses the information coming from multiple sensors and merges different state space models. So it can be used to eliminate system and measurement noise and estimate value of position and heading of mobile robot, On simulation experiments, different cases, such as single sensor and multi-sensor data fusion, were compared. The results demonstrate the effectiveness of this algorithm and exhibits good tracking performance.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2009年第19期6183-6185,6189,共4页
Journal of System Simulation
基金
国家留学基金委政府互换奖学金项目(20053303)
作者简介
刘国成（1972-），男，湖北枣阳人，博士，研究方向为非线性滤波与数据融合。
