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In this paper, we consider the problem of acoustic source localization byacoustic sensor networks (ASNs) using a promising, learning-based techniquethat adapts to the acoustic environment. In particular, we look at the scenariowhen a node in the ASN is displaced from its position during training. As themismatch between the ASN used for learning the localization model and the oneafter a node displacement leads to erroneous position estimates, a displacementhas to be detected and the displaced nodes need to be identified. We propose amethod that considers the disparity in position estimates made byleave-one-node-out (LONO) sub-networks and uses a Markov random field (MRF)framework to infer the probability of each LONO position estimate beingaligned, misaligned or unreliable while accounting for the noise inherent tothe estimator. This probabilistic approach is advantageous over naive detectionmethods, as it outputs a normalized value that encapsulates conditionalinformation provided by each LONO sub-network on whether the reading is inmisalignment with the overall network. Experimental results confirm that theperformance of the proposed method is consistent in identifying compromisednodes in various acoustic conditions.

Misalignment Recognition in Acoustic Sensor Networks using a Semi-supervised Source Estimation Method and Markov Random Fields