An interest is often present in knowing evolving variables that are not directly observable; this is the case in aerospace, engineering\ncontrol, medical imaging, or data assimilation.What is at hand, though, are time-varying measured data, a model connecting them\nto variables of interest, and a model of how to evolve the variables over time.However, both models are only approximation and the\nobserved data are tainted with noise. This is an ill-posed inverse problem.Methods, such as Kalman filter (KF), have been devised to\nextract the time-varying quantities of interest.These methods applied to this inverse problem, nonetheless, are slow, computation\nwise, since they require large matrices multiplications and even matrix inversion. Furthermore, these methods are not usually\nsuitable to impose some constraints. This article introduces a new iterative filtering algorithm based on alternating projections.\nExperiments were run with simulated moving projectiles and were compared with results using KF. The new optimization algorithm\nproves to be slightly more accurate than KF, but, more to the point, it is much faster in terms of CPU time.
Loading....