  Achieving quantum-enhanced performances when measuring unknown quantities
requires developing suitable methodologies for practical scenarios, that
include noise and the availability of a limited amount of resources. Here, we
report on the optimization of sub-standard quantum limit Bayesian
multiparameter estimation in a scenario where a subset of the parameters
describes unavoidable noise processes in an experimental photonic sensor. We
explore how the optimization of the estimation changes depending on which
parameters are either of interest or are treated as nuisance ones. Our results
show that optimizing the multiparameter approach in noisy apparata represents a
significant tool to fully exploit the potential of practical sensors operating
beyond the standard quantum limit for broad resources range.
