Title :
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
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;
Conference_Titel :
Aerospace and Electronics Conference, 1988. NAECON 1988., Proceedings of the IEEE 1988 National
Conference_Location :
Dayton, OH
DOI :
10.1109/NAECON.1988.195036
