A method of reactive zoom control from uncertainty in tracking
Computer Vision and Image Understanding
Recursive fading memory filtering
Information Sciences: an International Journal
Comparison of adaptive filters for gas turbine performance monitoring
Journal of Computational and Applied Mathematics
Adaptive algorithms for sparse system identification
Signal Processing
Suboptimal estimation of systems with large parameter uncertainties
Automatica (Journal of IFAC)
Brief paper: Adaptive sequential estimation with applications
Automatica (Journal of IFAC)
System identification-A survey
Automatica (Journal of IFAC)
Recursive bayesian estimation using gaussian sums
Automatica (Journal of IFAC)
On accurate localization and uncertain sensors
International Journal of Intelligent Systems
| Hi-index | 22.15 |
Applications of the Kalman filter in orbit determination problems have sometimes encountered a difficulty which has been referred to as divergence. The phenomenon is a growth in the residuals; the state and its estimate diverge. This problem can often be traced to insufficient accuracy in modeling the dynamics used in the filter. Although more accurate modeling is an obvious solution, it is often an impractical, and sometimes an impossible, one. Model errors are here approximated by a white, Gaussian noise input, and its covariance (Q) is determined so as to produce consistency between residuals and their statistics. In this way, realtime feedback is provided from the residuals to the filter gain. Onset of divergence produces an increase in the filter gain and the adaptive filter is able to continue tracking. This scheme has a probabilistic interpretation. Under certain conditions the estimate of Q produces the most probable finite sequence of residuals.