Improved nonlinear observable degree analysis using data fusion

作者：

Highlights：

• Distributed data fusion technology is used to construct a novel computation frame of the OD by use of two OD computation methods such as the Cramer-Rao Lower Bound and the Lie derivative.

• The fused pseudo-observation matrix and pseudo-state transition matrix are successfully designed, and the associated weighted fusion coefficients are taken by proposing a recursive method based on the condition number.

• Simulation is demonstrated to validate the proposed computation method on the observable degree of nonlinear estimation systems.

摘要

•Distributed data fusion technology is used to construct a novel computation frame of the OD by use of two OD computation methods such as the Cramer-Rao Lower Bound and the Lie derivative.•The fused pseudo-observation matrix and pseudo-state transition matrix are successfully designed, and the associated weighted fusion coefficients are taken by proposing a recursive method based on the condition number.•Simulation is demonstrated to validate the proposed computation method on the observable degree of nonlinear estimation systems.

论文关键词：Nonlinear system,Observable degree,Cramer-Rao lower bound,Condition number

论文评审过程：Received 15 August 2019, Revised 20 July 2020, Accepted 9 August 2020, Available online 2 October 2020, Version of Record 2 October 2020.

论文官网地址：https://doi.org/10.1016/j.amc.2020.125613