Covariance intersection-based sensor fusion for sounding rocket tracking and impact area prediction

The estimation of object motion from radar measurements is critical in applications such as air traffic control, airborne surveillance systems, launching of sounding rockets and orbital vehicles, and impact point prediction (IPP). This paper investigates the use of covariance intersection (CI) for the fusion of data from a pair of distinct radar sites at Alcantara Launch Center (ALC) to track a sounding rocket and predict the impact point and its uncertainty area on the ground in compliance with safety-of-flight issues. Debiased measurement transformation from spherical to cartesian coordinates, boost and free-fall models embedded in Kalman filters, and multiple hypothesis testing for multiple-model adaptive estimation are employed by the processing node at each radar site to locally estimate position, velocity, and acceleration. The local estimates and the corresponding computed covariance matrices from the radar sites are transformed to a common reference frame at the launch-pad and CI-fused. For the purpose of comparison, measurement fusion and track-to-track fusion are also evaluated. Prediction of the impact area with a given probability assumes free fall and considers the uncertainties inferred from the eigenvalue-eigenvector decomposition of the computed covariance matrix. All simulations presented herein make use of actual radar data of a Brazilian VS30 sounding rocket launched from ALC in February 2000. The automated fusion approach presents a much improved performance relative to the procedure then used at ALC for trajectory tracking and IPP, and qualitatively reproduces the expertise of the safety-of-flight officer. (c) 2006 Elsevier Ltd. All rights reserved.