Target Detection and Classification Using Seismic and PIR Sensors

Abstract

Unattended ground sensors (UGS) are widely used to monitor human activities, such as pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is often limited by high false alarm rates, possibly due to inadequacies of the underlying algorithms and limitations of onboard computation. In this regard, this paper presents a wavelet-based method for target detection and classification. The proposed method has been validated on data sets of seismic and passive infrared sensors for target detection and classification as well as for payload and movement type identification of the targets. The proposed method has the advantages of fast execution time and low memory requirements and is potentially well-suited for real-time implementation with onboard UGS systems.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2012
Accession Number
ADA562080

Entities

People

  • Asok Ray
  • Shalabh Gupta
  • Soumalya Sarkar
  • Thyagaraju Damarla
  • Xin Jin

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Data Analysis
  • Data Sets
  • Detection
  • Detectors
  • False Alarms
  • Feature Extraction
  • Frequency
  • Frequency Domain
  • Identification
  • Infrared Detectors
  • Sensor Networks
  • Supervised Machine Learning
  • Target Classification
  • Target Detection
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Sensor Fusion and Tracking Systems.