  Unlabeled sensing is a linear inverse problem where the measurements are
scrambled under an unknown permutation leading to loss of correspondence
between the measurements and the rows of the sensing matrix. Motivated by
practical tasks such as mobile sensor networks, target tracking and the pose
and correspondence estimation between point clouds, we study a special case of
this problem restricting the class of permutations to be local and allowing for
multiple views. In this setting, namely unlabeled multi-view sensing with local
permutation, previous results and algorithms are not directly applicable. In
this paper, we propose a computationally efficient algorithm that creatively
exploits the machinery of graph alignment and Gromov-Wasserstein alignment and
leverages the multiple views to estimate the local permutations. Simulation
results on synthetic data sets indicate that the proposed algorithm is scalable
and applicable to the challenging regimes of low to moderate SNR.
