Adaptive IMM-UKF for Airborne Tracking

• Published in: Aerospace 2023-08, Vol.10 (8), p.698
• Main Authors: Arroyo Cebeira, Alvaro, Asensio Vicente, Mariano
• Format: Article
• Language: English
• Subjects: Aerial targets; Aircraft; Algorithms; Collision avoidance; Consistency; Deep learning; Estimation errors; interacting multiple model; Kalman filters; maneuvering target; Maneuvers; Monte Carlo method; Monte Carlo simulation; Neural networks; Noise; Noise control; Noise reduction; Random variables; Surveillance; System theory; Tracking; trajectory estimation; Transition probabilities; Velocity
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace10080698

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.