Sequential LMMSE Filtering with Out-of-Sequence Observations under Nonlinear System

被引:0
|
作者
Lei, Ming [1 ]
Baehr, Cristophe [2 ]
Jing, Zhongliang [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai, Peoples R China
[2] Meteo France, Natl Meteorol Res Ctr, CNRS, Toulouse, France
来源
2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2018年
关键词
Out-of-sequence Measurement (OOSM); Linear Minimum Mean Squared Error (LMMSE); Arbitrary Constant Delay; Sensor Faults; Mixture LMMSE Estimator; ESTIMATOR; TRACKING; UPDATE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this work, the state estimation problem is taken into consideration in the simultaneous presence of sensor faults, out-of-sequence measurements (OOSM) and nonlinear dynamics. We derive the analytic formulae of linear minimum mean squared error (LMMSE) estimation with the arbitrary delay OOSMs and the observation faults, then outline a sequential LMMSE filtering under nonlinear dynamics. The sequential filtering lies first in fulfilling a nonlinear estimation using insequence observations, secondly perform a LMMSE correction to the nonlinear estimates with OOSMs. The proposed approach's feasibility is demonstrated by numerical comparisons.
引用
收藏
页码:2187 / 2194
页数:8
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