  In this paper, we consider the problem of acoustic source localization by
acoustic sensor networks (ASNs) using a promising, learning-based technique
that adapts to the acoustic environment. In particular, we look at the scenario
when a node in the ASN is displaced from its position during training. As the
mismatch between the ASN used for learning the localization model and the one
after a node displacement leads to erroneous position estimates, a displacement
has to be detected and the displaced nodes need to be identified. We propose a
method that considers the disparity in position estimates made by
leave-one-node-out (LONO) sub-networks and uses a Markov random field (MRF)
framework to infer the probability of each LONO position estimate being
aligned, misaligned or unreliable while accounting for the noise inherent to
the estimator. This probabilistic approach is advantageous over naive detection
methods, as it outputs a normalized value that encapsulates conditional
information provided by each LONO sub-network on whether the reading is in
misalignment with the overall network. Experimental results confirm that the
performance of the proposed method is consistent in identifying compromised
nodes in various acoustic conditions.
