Symbolic dynamic filtering of seismic sensors for target detection and classification

Xin Jin, Shalabh Gupta, Asok Ray, Thyagaraju Damarla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


Seismic sensors are widely used to monitor human activities, such as pedestrian motion and detection of intruders in a secure region. This paper presents a symbolic dynamics-based method of data-driven pattern classification by extracting the embedded information from noise-contaminated sensor time series. In the proposed method, the wavelet transforms of sensor data are partitioned to construct symbol sequences. Subsequently, the relevant information is extracted via construction of probabilistic finite state automata (PFSA) from symbol sequences. The patterns are derived from individual PFSA and are subsequently classified to make decisions on target classification. The proposed method has been validated on field data from seismic sensors to monitor infiltration of humans, light vehicles, and animals. The results of pattern classification demonstrate low false-alarm/missed-detection rate in target detection and high rate of correct target classification.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Number of pages6
StatePublished - 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2011 American Control Conference, ACC 2011
Country/TerritoryUnited States
CitySan Francisco, CA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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