Speech Coding Algorithms: Foundation and Evolution of Standardized Coders
Speech Coding Algorithms: Foundation and Evolution of Standardized Coders
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Kalman Filtering: with Real-Time Applications
Kalman Filtering: with Real-Time Applications
| Hi-index | 22.14 |
In this paper the estimation and attitude control problem of a rigid body spacecraft system with loss of observation is addressed. While a number of estimation algorithms are widely utilised in real-time applications, most of them are inadequate in the event of loss of observation as they are fundamentally based on the plant dynamics relying heavily on the measured output data. To overcome this shortcoming, a compensated closed-loop estimation algorithm is suggested in this work and is implemented in a spacecraft system with intermittent measured signals. The compensated observation signal, reconstructed using a linear prediction subsystem, is supplied at the measurement update step in the Kalman filtering. To limit the number of observations utilised in the linear prediction filter, a minimum mean square error based scheme is provided to obtain the size of linear prediction filter order. A Lyapunov-stability based output feedback control scheme is employed for the stabilisation problem. The simulation results demonstrate the effectiveness of the compensated algorithm wherein the aim is tackling the estimation problem subject to loss of measurements for the spacecraft application.