摘要
提出了一种基于聚类分析和Kalman滤波相结合的多传感器航迹起始算法。根据多传感器同一时刻对同一目标的观测值在空间呈团状的特征,运用聚类的方法解决数据融合问题。采用一种改进的粒子群(PSO)优化算法对多传感器观测数据进行聚类,结合聚类中心和目标预测值,应用Kalman滤波器估计目标状态,从而实现航迹起始。实验结果表明,该方法有效。
An algorithm of the multi-sensor data fusion for the track initiation is presented based on clustering analysis and Kalman filtering. The feature that the measurements of the same target at the same time have spherical shape makes it possible to use the clustering technique to solve the data fusion problem. An improved PSO algorithm is used to cluster the observation data, combine the cluster centers with the predicted target values, estimate the state of the targets with the Kalman filter and realized the track initiation. The experiment results show that the method is effective.
出处
《成都信息工程学院学报》
2009年第4期366-369,共4页
Journal of Chengdu University of Information Technology
基金
国家自然科学基金资助项目(60672099)
