Approximate analytical solutions for the weight optimization problems of CI and ICI

Approximate analytical formulae are proposed for the solutions of the weight optimization problems involved in Covariance Intersection (CI) and Inverse Covariance Intersection (ICI). The methodology used for obtaining the analytic approximations boils down to using just two Newton iterations with the initial weight value 1/2. The simulation results show that quite acceptable root-mean-square (RMS) error levels are achievable with the proposed approximate analytical weights with less computations compared to what would be required when numerical optimization with a high termination tolerance is used, which can be critical for applications with very limited computational resources.

[1]  Jeffrey K. Uhlmann,et al.  General Decentralized Data Fusion With Covariance Intersection (CI) , 2001 .

[2]  Kaare Brandt Petersen,et al.  The Matrix Cookbook , 2006 .

[3]  Jeffrey K. Uhlmann,et al.  A non-divergent estimation algorithm in the presence of unknown correlations , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[4]  Pablo O. Arambel,et al.  Estimation under unknown correlation: covariance intersection revisited , 2002, IEEE Trans. Autom. Control..

[5]  M. Hurley An information theoretic justification for covariance intersection and its generalization , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[6]  Yaakov Bar-Shalom,et al.  The Effect of the Common Process Noise on the Two-Sensor Fused-Track Covariance , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Ronald P. S. Mahler,et al.  Optimal/robust distributed data fusion: a unified approach , 2000, SPIE Defense + Commercial Sensing.

[8]  Uwe D. Hanebeck,et al.  Decentralized data fusion with inverse covariance intersection , 2017, Autom..