  Scalable and decentralized algorithms for Cooperative Self-localization (CS)
of agents, and Multi-Target Tracking (MTT) are important in many applications.
In this work, we address the problem of Simultaneous Cooperative
Self-localization and Multi-Target Tracking (SCS-MTT) under target data
association uncertainty, i.e., the associations between measurements and target
tracks are unknown. Existing CS and tracking algorithms either make the
assumption of no data association uncertainty or employ a hard-decision rule
for measurement-to-target associations. We propose a novel decentralized
SCS-MTT method for an unknown and time-varying number of targets under
association uncertainty. Marginal posterior densities for agents and targets
are obtained by an efficient belief propagation (BP) based scheme while data
association is handled by marginalizing over all target-to-measurement
association probabilities. Decentralized single Gaussian and Gaussian mixture
implementations are provided based on average consensus schemes, which require
communication only with one-hop neighbors. An additional novelty is a
decentralized Gibbs mechanism for efficient evaluation of the product of
Gaussian mixtures. Numerical experiments show the improved CS and MTT
performance compared to the conventional approach of separate localization and
target tracking.
