Outlier Detection from Non-Smooth Sensor Data

Timo Huuhtanen, Henrik Ambos, Alex Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

27 Downloads (Pure)


Outlier detection is usually based on smooth assumption of the data. Most existing approaches for outlier detection from spatial sensor data assume the data to be a smooth function of the location. Spatial discontinuities in the data, such as arising from shadows in photovoltaic (PV) systems, may cause outlier detection methods based on the spatial smoothness assumption to fail. In this paper, we propose novel approaches for outlier detection of non-smooth spatial data. The methods
are evaluated by numerical experiments involving PV panel measurements as well as synthetic data.
Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
Number of pages5
ISBN (Electronic)9789082797039
Publication statusPublished - 5 Sep 2019
MoE publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference - Coruna, Spain
Duration: 2 Sep 20196 Sep 2019

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO


  • outlier detection
  • spatial signals

Fingerprint Dive into the research topics of 'Outlier Detection from Non-Smooth Sensor Data'. Together they form a unique fingerprint.

  • Cite this

    Huuhtanen, T., Ambos, H., & Jung, A. (2019). Outlier Detection from Non-Smooth Sensor Data. In EUSIPCO 2019 - 27th European Signal Processing Conference (pp. 1-5). (European Signal Processing Conference). IEEE. https://doi.org/10.23919/EUSIPCO.2019.8903061