|
Digital
Library of the European Council for Modelling
and Simulation |
Title: |
Modelling Of The Underwater Targets Tracking With The
Aid Of Pseudomeasurements Kalman Filter |
Authors: |
Alexander E.
Miller, Boris M. Miller |
Published in: |
(2017).ECMS 2017 Proceedings
Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás
Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi:10.7148/2017 ISBN:
978-0-9932440-4-9/ ISBN:
978-0-9932440-5-6 (CD) 31st European Conference on Modelling and
Simulation, Budapest, Hungary, May 23rd
– May 26th, 2017 |
Citation
format: |
Alexander
E. Miller, Boris M. Miller (2017). Modelling Of The Underwater Targets
Tracking With The Aid Of Pseudomeasurements Kalman Filter, ECMS 2017
Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi,
Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics
European Council for Modeling and Simulation. doi:
10.7148/2017-0615 |
DOI: |
https://doi.org/10.7148/2017-0615 |
Abstract: |
Target
motion analysis of the underwater target tracking by the UUV (Unmanned
underwater vehicle) usually based on the bearing-only observations including
azimuth and elevation angles. However, low angular resolution of hydro
acoustic sonars is not enough for the good quality of tracking. Moreover,
angular observations lead to nonlinear filtering such as Extended Kalman
Filtering (EKF) which usually produces estimations
with unknown bias and quadratic errors. As it was mentioned long ago in a
case of bearing-only observations target unobservability may take place,
therefore, some special observer’s motion become necessary. Other filters
like the particle or unscented ones need the additional computer resources
and also may produce the tracking loss. At the same time the
pseudomeasurements Kalman filtering (PKF) method which transforms the
estimation problem to the linear one and gives the current coordinates
estimation with almost same accuracy could be modified to evaluate the moving
target coordinates and velocities without bias. Since PKF gives unbiased
estimate for the motion and the quadratic error it provides the good means
for integration of various measurements methods such as passive
(bearing-only) and active (range) metering. Using this filtering approach the
good quality of target motion analysis (TMA) for randomly moving target may
be achieved. |
Full
text: |