  Data association is one of the fundamental problems in multi-sensor systems.
Most current techniques rely on pairwise data associations which can be
spurious even after the employment of outlier rejection schemes. Considering
multiple pairwise associations at once significantly increases accuracy and
leads to consistency. In this work, we propose two fully decentralized methods
for consistent global data association from pairwise data associations. The
first method is a consensus algorithm on the set of doubly stochastic matrices.
The second method is a decentralization of the spectral method proposed by
Pachauri et al.. We demonstrate the effectiveness of both methods using
theoretical analysis and experimental evaluation.
