AN EFFICIENT PARTICLE FILTERING TECHNIQUE ON THE GRASSMANN MANIFOLD

Subspace tracking methods are widespread in signal and image processing. To reduce the influence of perturbations or outliers on the measurements, some authors have used a stochastic piecewise constant velocity model on the Grassmann manifold. This paper presents an efficient way to simulate such a model using a particular representation of the Grassmann manifold. By doing so, we can reduce the spatial and time complexity of filtering techniques based on this model. We also propose an approximation of this system which can be computed in a finite number of operations and show similar results if the subspace variation is slow.