Adaptive State Estimation for a System With Unknown Input and Measurement Bias

An adaptive state estimator for passive underwater tracking of maneuvering targets is developed. The state estimator is designed specifically for a system containing independent unknown or randomly switching input and measurement biases. In modeling the stochastic system, it is assumed that the bias...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of oceanic engineering 1987-01, Vol.12 (1), p.222-227
Main Authors: Moose, R., Sistanizadeh, M., Skagfjord, G.
Format: Article
Language:English
Subjects:
Acoustic signal processing
Acoustics
Bayesian methods
Covariance matrix
Equations
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Gaussian noise
Marine
Markov processes
Noise measurement
parameter estimation
passive sonar
Physics
signal processing
sonar detection
State estimation
Time measurement
tracking
underwater technology
Underwater tracking
Vectors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An adaptive state estimator for passive underwater tracking of maneuvering targets is developed. The state estimator is designed specifically for a system containing independent unknown or randomly switching input and measurement biases. In modeling the stochastic system, it is assumed that the bias sequence dynamics for both input and measurement 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 has been developed. Despite the large and randomly varying biases, the proposed estimator provides an accurate estimate of the system states.
ISSN:0364-9059
1558-1691
DOI:10.1109/JOE.1987.1145235