  We propose a Kullback-Leibler Divergence (KLD) filter to extract anomalies
within data series generated by a broad class of proximity sensors, along with
the anomaly locations and their relative sizes. The technique applies to
devices commonly used in engineering practice, such as those mounted on mobile
robots for non-destructive inspection of hazardous or other environments that
may not be directly accessible to humans. The raw data generated by this class
of sensors can be challenging to analyze due to the prevalence of noise over
the signal content. The proposed filter is built to detect the difference of
information content between data series collected by the sensor and baseline
data series. It is applicable in a model-based or model-free context. The
performance of the KLD filter is validated in an industrial-norm setup and
benchmarked against a peer industrially-adopted algorithm.
