摘要
基于贝叶斯估计的平滑算法是在事后处理的情况下,依据过去直至现在的观测值去估计过去的历史状态,以有效提高精度。而滤波是依据过去直至现在的观测值去估计现在的状态。从理论上讲,由于平滑用了所求估计时刻之后的观测值,平滑算法应比滤波优异一些。本文设计了2个实验仿真计算,在逼近效果和RMS等方面分别与Kalman滤波和双向滤波加权平均进行了比较。经实验证明,无论是逼近效果还是RMS,平滑算法都要更优一些。
A smoothing algorithm is based on Bayesian theory, which should be derived using the Kalman filtering. In the case of post-process, the precision can be improved by estimation using observations from past to future. The filter algorithm uses the observa- tions from past to now. So, in theory, the smoothing algorithm is better than the filter algorithm in precision. Two simulation experi- ments were taken in the paper, and the results showed that the smoothing algorithm is more effective than Kalman filtering and a weigh- ted combination of forward and backward filtering.
出处
《测绘科学》
CSCD
北大核心
2013年第3期52-53,51,共3页
Science of Surveying and Mapping
基金
信息工程大学测绘学院硕士学位论文创优基金(201112)
关键词
贝叶斯估计
平滑算法
KALMAN滤波
双向滤波加权平均
Bayes estimation
smoothing algorithm
Kalman filtering
a weighted combination of forward and backward filtering
