Distributed Estimation with Partially Overlapping States based on Deterministic Sample-based Fusion

Distributing workload between sensor nodes is a practical solution to monitor large-scale phenomena. In doing so, the system can be split into smaller subsystems that can be estimated and controlled more easily. While current state-of-the-art fusion methods for distributed estimation assume the fusion of estimates referring to the full dimension of the state, little effort has been made to account for the fusion of unequal state vectors referring to smaller subsystems of the full system. In this paper, a novel method to fuse overlapping state vectors using a deterministic sample-based fusion method is proposed. These deterministic samples can be used to account for the correlated and uncorrelated noise terms and are therefore able to reconstruct the joint covariance matrix in a distributed fashion. The performance of the proposed fusion method is compared to other state-of-the-art methods.

[1]  Uwe D. Hanebeck,et al.  Fusion Strategies for Unequal State Vectors in Distributed Kalman Filtering , 2014 .

[2]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[3]  Simon J. Julier,et al.  The spherical simplex unscented transformation , 2003, Proceedings of the 2003 American Control Conference, 2003..

[4]  Uwe D. Hanebeck,et al.  Reconstruction of joint covariances in networked linear systems , 2014, 2014 48th Annual Conference on Information Sciences and Systems (CISS).

[5]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.

[6]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[7]  Chee-Yee Chong,et al.  Forty years of distributed estimation: A review of noteworthy developments , 2017, 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF).

[8]  S.S. Stankovic,et al.  Consensus Based Overlapping Decentralized Estimator , 2007, 2007 American Control Conference.

[9]  Uwe D. Hanebeck,et al.  Optimal sample-based fusion for distributed state estimation , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[10]  Ondrej Straka,et al.  Reconstruction of Cross-Correlations with Constant Number of Deterministic Samples , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[11]  Chee-Yee Chong,et al.  Distributed Tracking in Distributed Sensor Networks , 1986 .

[12]  Shu-Li Sun,et al.  Multi-sensor optimal information fusion Kalman filter , 2004, Autom..

[13]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[14]  Jonatan Ralf Axel Klemets,et al.  Hierarchical Decentralized State Estimation with Unknown Correlation for Multiple and Partially Overlapping State Vectors , 2018, 2018 IEEE Conference on Control Technology and Applications (CCTA).

[15]  Mircea Lazar,et al.  State fusion with unknown correlation: Ellipsoidal intersection , 2010, Proceedings of the 2010 American Control Conference.

[16]  Uwe D. Hanebeck,et al.  Comparative Study of Track-to-Track Fusion Methods for Cooperative Tracking with Bearings-only Measurements , 2019, 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS).

[17]  Y. Bar-Shalom.  On the track-to-track correlation problem , 1981 .

[18]  D. Siljak,et al.  Decentralized control with overlapping information sets , 1981 .

[19]  X. Rong Li,et al.  Practical Measures and Test for Credibility of an Estimator , 2001 .

[20]  Uwe D. Hanebeck,et al.  An empirical method to fuse partially overlapping state vectors for distributed state estimation , 2013, 2013 European Control Conference (ECC).

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

[22]  Giorgio Battistelli,et al.  Distributed Finite-Element Kalman Filter for Field Estimation , 2017, IEEE Transactions on Automatic Control.

[23]  James Llinas,et al.  Handbook of Multisensor Data Fusion : Theory and Practice, Second Edition , 2008 .

[24]  Dan C. Marinescu,et al.  Data incest in cooperative localisation with the Common Past-Invariant Ensemble Kalman filter , 2013, Proceedings of the 16th International Conference on Information Fusion.

[25]  Uwe D. Hanebeck,et al.  Treatment of dependent information in multisensor kalman filtering and data fusion , 2017 .