ADAPTIVE ESTIMATION FOR A SYSTEM WITH UNKNOWN MEASUREMENT BIAS.

被引：0

作者：

Sistanizadeh, Mohammad K. ^{[1
]}

Skagfjord, Gisli ^{[1
]}

Moose, Richard L. ^{[1
]}

机构：

[1] Virginia Polytechnic Inst &, State Univ, Blacksburg, VA, USA, Virginia Polytechnic Inst & State Univ, Blacksburg, VA, USA

来源：

IEEE Transactions on Aerospace and Electronic Systems | 1986年 / AES-22卷 / 06期

关键词：

SIGNAL FILTERING AND PREDICTION - Mathematical Models;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

An adaptive state estimator is developed for passive underwater tracking of maneuvering targets. The state estimator is designed specifically for a system containing unknown or randomly switching biased measurements. In modeling the stochastic system, it is assumed that the bias sequence dynamics can be modeled by a semi-Markov process. By incorporating the semi-Markovian concept into a Bayesian estimation technique, an estimator consisting of a bank of parallel, adaptively weighted, Kalman filters was developed. Despite the large and randomly varying measurement biases, the proposed estimator provides an accurate estimate of the system states.

引用

页码：732 / 739