𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Sensitivities of array filters to variations in filter parameters

✍ Scribed by Z.W Sheng; M Simaan

Publisher
Elsevier Science
Year
1987
Tongue
English
Weight
963 KB
Volume
13
Category
Article
ISSN
0165-1684

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Pole-zero sensitivities of a digital fil
Pole-zero sensitivities of a digital filter due to parameter quantization
✍ M. R. StojiΔ‡; R. M. StojiΔ‡ πŸ“‚ Article πŸ“… 1977 πŸ› John Wiley and Sons 🌐 English βš– 333 KB

## Abstract An analytical procedure for the sensitivity analysis of digital filters is presented. The procedure applies to the sensitivity investigation of digital‐filter poles and zeros due to parameter quantization. Suitably defined sensitivities and a computer‐aided technique, presented in the p

Sensitivity of AUV response to variation
Sensitivity of AUV response to variations in hydrodynamic parameters
✍ Doug Perrault; Neil Bose; Siu O’Young; Christopher D. Williams πŸ“‚ Article πŸ“… 2003 πŸ› Elsevier Science 🌐 English βš– 424 KB

The sensitivity of the response of a typical AUV to changes in hydrodynamic parameters is examined. The analysis is primarily performed using a computer model of an axi-symmetric vehicle typical of many AUVs in service today. The vehicle used is the Canadian Self-Contained Off-the-shelf Underwater T

Comparison of linear distributed-paramet
Comparison of linear distributed-parameter filters to lumped approximants
✍ D. J. Cooper; W. F. Ramirez; D. E. Clough πŸ“‚ Article πŸ“… 1986 πŸ› American Institute of Chemical Engineers 🌐 English βš– 851 KB

## Comparison of Linear Distributed-Parameter Filters to Lumped Approximants Optimal distributed-parameter filters are commonly implemented using approximating lumped Kalman filtering theory. The effect of such an approximation is investigated. A theoretical development shows that there is a loss

Sensitivity of discrete-time Kalman filt
Sensitivity of discrete-time Kalman filter to statistical modeling errors
✍ Samer S. Saab; George E. Nasr πŸ“‚ Article πŸ“… 1999 πŸ› John Wiley and Sons 🌐 English βš– 113 KB πŸ‘ 1 views

The optimum "ltering results of Kalman "ltering for linear dynamic systems require an exact knowledge of the process noise covariance matrix Q I , the measurement noise covariance matrix R I and the initial error covariance matrix P . In a number of practical solutions, Q I , R I and P , are either