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- Title
Adaptive IMM-UKF for Airborne Tracking.
- Authors
Arroyo Cebeira, Alvaro; Asensio Vicente, Mariano
- Abstract
In this paper, we propose a nonlinear tracking solution for maneuvering aerial targets based on an adaptive interacting multiple model (IMM) framework and unscented Kalman filters (UKFs), termed as AIMM-UKF. The purpose is to obtain more accurate estimates, better consistency of the tracker, and more robust prediction during sensor outages. The AIMM-UKF framework provides quick switching between two UKFs by adapting the transition probabilities between modes based on a distance function. Two modes are implemented: a uniform motion model and a maneuvering model. The experimental validation is performed with Monte Carlo simulations of three scenarios with ACAS Xa tracking logic as a benchmark, which is the next generation of airborne collision avoidance systems. The two algorithms are compared using hypothesis testing of the root mean square errors. In addition, we determine the normalized estimation error squared (NEES), a new proposed noise reduction factor to compare the estimation errors against the measurement errors, and an estimated maximum error of the tracker during sensor dropouts. The experimental results illustrate the superior performance of the proposed solution with respect to the tracking accuracy, consistency, and expected maximum error.
- Subjects
MONTE Carlo method; STANDARD deviations; TRACKING algorithms; KALMAN filtering; MEASUREMENT errors; NOISE control; ARTIFICIAL satellite tracking; DISTANCES
- Publication
Aerospace (MDPI Publishing), 2023, Vol 10, Issue 8, p698
- ISSN
2226-4310
- Publication type
Article
- DOI
10.3390/aerospace10080698