摘要
以GPS动态滤波方法为研究对象,首先采用了多尺度分析方法,将机动载体的"当前"统计模型与基于统计特性的多尺度信号变换方法相结合;然后结合常规卡尔曼滤波算法,建立起GPS动态滤波的多尺度数据融合估计新算法;最终在最细尺度上获得目标状态基于全局信息的融合估计值。将上述算法应用于GPS的动态滤波中,仿真结果表明,该算法相对于原始算法可有效提高目标状态的估计精度。
Taking GPS dynamic wave-filtering method as study object,this paper combined the ＂current＂ statistical model of automotive carrier with the multi-scale signal transformation based on statistical characteristic by using the multi-scale analysis and established a new algorithm of multi-scale data fusion for GPS dynamic wave-filtering combining with normal Kalman wave-filtering algorithm.At last,the paper achieved the optimal fusion estimation value of the target states based on global information at the finest scale.The simulated results showed that the proposed algorithm could effectively increase the estimation precision of target states compared with the conventional algorithm.
出处
《山东科技大学学报（自然科学版）》
CAS
2010年第6期9-13,共5页
Journal of Shandong University of Science and Technology(Natural Science)
基金
国家自然科学基金项目(60874112)
山东省"泰山学者"建设工程专项经费项目
关键词
GPS动态定位
多尺度估计
动态滤波方法
小波变换
GPS dynamic positioning
multi-scale estimation
dynamic wave-filtering method
wavelet transformation.
