Stochastic adaptive tracker based on noise-corrupted space-time measurement process

Author

Johnson, Bruce A. ; Maybeck, Peter S.

Author_Institution

Dept. of Electr. & Comput. Eng., US Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA

fYear

1988

fDate

23-27 May 1988

Firstpage

352

Abstract

A multiple model adaptive estimator (MMAE) is formulated to estimate the state of a dynamic system modeled by a linear stochastic differential equation, from which the feedback observations are described as a noise-corrupted Poisson space-time point process. Then the MMAE is embedded into a stochastic adaptive PI (proportional-plus-integral) tracker using LQG (linear quadratic Gaussian) and assumed certainty equivalence techniques. The MMAE, the Kalman filter (used to estimate the target state), and the tracker are evaluated for parameter sensitivity, robustness, and adaptation using Monte Carlo simulation

Keywords

Kalman filters; Monte Carlo methods; adaptive systems; differential equations; feedback; filtering and prediction theory; stochastic systems; tracking systems; two-term control; Kalman filter; Monte Carlo simulation; PI tracker; adaptation; certainty equivalence techniques; feedback; linear quadratic Gaussian technique; linear stochastic differential equation; multiple model adaptive estimator; noise-corrupted Poisson space-time point process; noise-corrupted space-time measurement; parameter sensitivity; robustness; stochastic adaptive tracker; two-term control; Filters; Force measurement; Laser beams; Noise measurement; Particle beams; Signal processing; State estimation; Stochastic resonance; Stochastic systems; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace and Electronics Conference, 1988. NAECON 1988., Proceedings of the IEEE 1988 National

Conference_Location

Dayton, OH

Type

conf

DOI

10.1109/NAECON.1988.195036

Filename

195036