A comparison of nonlinear filters and multi-sensor fusion for tracking boost-phase ballistic missiles
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Authors
Kim, Kyungsu.
Pace, Phillip E.
Hutchins, Robert G.
Michael, James Bret
Subjects
Boost-phase Missile Defense, Impulse Modeling, IMPULSE, RF sensors, Multiple Hypotheses Tracking, Extended Kalman Filtering, Unscented Kalman Filtering, Particle Filtering, Unscented Particle Filtering, Multi-sensor fusion, Extended Information Filter.
Advisors
Date of Issue
2009-01
Date
2009-01
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
This report studies two aspects of tracking ballistic missiles during boost phase. The first part compares the performance of several nonlinear filtering algorithms in tracking a single target: the extended Kalman filter (EKF); the unscented Kalman filter (UKF); the particle filter (PF); and the particle filter with UFK update (UPF). Measurements are range, azimuth and elevation. In the absence of measurement error, all algorithms work well except for the PF, which does not converge. With measurement noise (standard deviations of 10 meters and 1 degree) the EFK also performs poorly, while the UPF is the top performer (although it is also the most computationally intensive). The second part compares the extended information filter (EIF) with earlier work on track scoring to perform sensor/data fusion in a multi-hypothesis framework. Here we find that the EIF handily outperformed other fusion algorithms based on track scoring that we tested
Type
Technical Report
Description
Series/Report No
Department
Organization
Missile Defense Agency (U.S.)
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
NPS-CS-09-002
Sponsors
Prepared for: U.S. Missile Defense Agency
Funder
Format
ix, 80 p. : col. ill. ; 28 cm.