Target detection and target type & motion classification: Comparison of feature extraction algorithms

Yue Li, Asok Ray, Thomas A. Wettergren

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

    2 Citations (Scopus)

    Abstract

    This paper addresses sensor network-based surveillance of target detection and target type & motion classification. The performance of target detection and classification could be compromised (e.g., due to high rates of false alarm and misclassification), because of inadequacies of feature extraction from (possibly noisy) sensor data and subsequent pattern classification over the network. A feature extraction algorithm, called symbolic dynamic filtering (SDF), is investigated for solving the target detection & classification problem. In this paper, the performance of SDF is compared with two commonly used feature extractors, namely, Cepstrum and principal component analysis (PCA)). Each of these three feature extractors is executed in conjunction with three well-known pattern classifiers, namely, k-nearest neighbor (k-NN), support vector machine (SVM), and sparse representation classification (SRC). Results of numerical simulation are presented based on a dynamic model of target maneuvering and passive sonar sensing in the ocean environment. These results show that SDF has a consistently superior performance for all tasks - target detection and target type & motion classification.

    Original languageEnglish (US)
    Title of host publication2014 American Control Conference, ACC 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1132-1137
    Number of pages6
    ISBN (Print)9781479932726
    DOIs
    StatePublished - Jan 1 2014
    Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
    Duration: Jun 4 2014Jun 6 2014

    Other

    Other2014 American Control Conference, ACC 2014
    CountryUnited States
    CityPortland, OR
    Period6/4/146/6/14

    Fingerprint

    Target tracking
    Feature extraction
    Sonar
    Principal component analysis
    Sensor networks
    Pattern recognition
    Support vector machines
    Dynamic models
    Classifiers
    Sensors
    Computer simulation

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    Li, Y., Ray, A., & Wettergren, T. A. (2014). Target detection and target type & motion classification: Comparison of feature extraction algorithms. In 2014 American Control Conference, ACC 2014 (pp. 1132-1137). [6858726] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2014.6858726
    Li, Yue ; Ray, Asok ; Wettergren, Thomas A. / Target detection and target type & motion classification : Comparison of feature extraction algorithms. 2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1132-1137
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    Li, Y, Ray, A & Wettergren, TA 2014, Target detection and target type & motion classification: Comparison of feature extraction algorithms. in 2014 American Control Conference, ACC 2014., 6858726, Institute of Electrical and Electronics Engineers Inc., pp. 1132-1137, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6858726

    Target detection and target type & motion classification : Comparison of feature extraction algorithms. / Li, Yue; Ray, Asok; Wettergren, Thomas A.

    2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1132-1137 6858726.

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

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    Li Y, Ray A, Wettergren TA. Target detection and target type & motion classification: Comparison of feature extraction algorithms. In 2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1132-1137. 6858726 https://doi.org/10.1109/ACC.2014.6858726