A comparison of nonlinear filters and multi-sensor fusion for tracking boost-phase ballistic missiles
By: by Kyungsu Kim ... [et al.].
Contributors: Pace, Phillip E.; Hutchins, Robert G.; Michael, James Bret; Naval Postgraduate School (U.S.); United States. Missile Defense Agency
Publisher: Naval Postgraduate School
Published: 2009
Place of publication: Monterey, California
Language: eng
Number of pages: 80
Subjects: Multisensor data fusion
Description: 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.
Notes: Title from cover.

"Prepared for: U.S. Missile Defense Agency"--Cover.

"January 2009"--Cover.

"NPS-CS-09-002"--Cover.

Author(s) subject terms: 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.

Includes bibliographical references (p. 77-79).

"Approved for public release; distribution unlimited"--Cover.

Technical report.

Funding number: MD7080101P0630.