  Irregular propagation environments with complex scattering effects challenge
traditional ray-tracing-based localization. However, the environment's
complexity enables solutions based on wave fingerprints (WFPs). Yet, since WFPs
rely on the extreme sensitivity of the chaotic wave field to geometrical
details, it is not clear how viable WFP techniques may be in a realistic
dynamically evolving environment. Here, we reveal that environmental
perturbations reduce both the diversity of the WFP dictionary and the effective
signal-to-noise ratio (SNR), such that the amount of information that can be
obtained per measurement is reduced. This unfavorable effect can, however, be
fully compensated by taking more measurements. We show in simulations and
experiments with a low-cost software-defined radio that WFP localization of
non-cooperative objects is possible even when the scattering strength of the
environmental perturbation significantly exceeds that of the object to be
localized. Our results underline that diversity is only one important
ingredient to achieve high sensing accuracy in compressed sensing, the other
two being SNR and the choice of decoding method. We find that sacrificing
diversity for SNR may be worthwhile and observe that artificial neural networks
outperform traditional decoding methods in terms of the achieved sensing
accuracy, especially at low SNR.
