摘要
针对带有精度分类信息的方差未知量测数据的滤波问题,本文提出了一种扩展的变分贝叶斯自适应Kalman滤波(EVB-AKF)算法。该算法在量测数据精度等级不变或降低时将后验分布参数修正为原VB-AKF算法外推近似值与精度分类信息对应的方差上界的加权和的形式,并在精度等级提高时利用精度分类信息重置后验分布参数,解决了VB-AKF算法后验分布参数一阶常系数模型不能完全适应量测噪声方差动态变化的问题。仿真结果表明,该算法能够快速有效的估计出动态变化的量测噪声方差,并且能够有效的实现数据滤波。
An extended variational Bayesian adaptive Kalman filtering （EVB-AKF） algorithm is presented for the filtering of measurement data corrupted by noise with unknown variance but accuracy category information.The proposed algorithm modifies the posterior distribution parameter as follow：when the accuracy level of measurement data does not change or decreases,it is assigned to the weighted sum of the extrapolate approximation of the posterior distribution parameter in VB-AKF and the upper variance bound in the accuracy category information; when accuracy level increases,it is reset to the upper variance bound in the accuracy category information.Thus the problem that the first-order constant coefficient model of the posterior distribution parameter in VB-AKF cannot fully adapt to the measurement noise variance dynamics is solved.Simulation results show that the proposed algorithm can estimate the dynamic varying measurement noise variance effectively and efficiently,thus achieve an effective data filtering.
出处
《信号处理》
CSCD
北大核心
2013年第11期1482-1487,共6页
Journal of Signal Processing
基金
国家科技支撑计划(2011BAH24B12)
教育部博士点基金(20124408110002)
武器装备预研项目(XXXX020602)
深圳市科技计划项目(JCYJ20130329105816574)
关键词
变分贝叶斯
精度分类信息
一阶模型
量测噪声方差估计
variational Bayesian
accuracy category information
first-order model
estimation of measurement noise variance
