Adaptive Estimation and Control
Adaptive Estimation and Control
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Distributed fusion receding horizon filtering in linear stochastic systems
EURASIP Journal on Advances in Signal Processing
Distributed receding horizon filtering in discrete-time dynamic systems
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Distributed fusion of local probability data association filters in multi-sensor environment
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A recursive fusion filter for angular data
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Derivation of centralized and distributed filters using covariance information
Computational Statistics & Data Analysis
Sequential covariance intersection fusion Kalman filter
Information Sciences: an International Journal
| Hi-index | 0.08 |
We derive an optimal combination of arbitrary number correlated estimates. In particular, for two estimates this combination represents the well-known Millman and Bar-Shalom-Campo formulae for uncorrelated and correlated estimation errors, respectively. This new result is applied to the various estimation problems as least-squares estimation, Kalman filtering, and adaptive filtering. The new approximate reduced-order estimators with parallel structure are presented. A practical implementation issue to consider these estimators is also addressed. Examples demonstrate the accuracy and efficiency of application of the generalized Millman's formula.