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End-to-End Classification Process for the Exploitation of Vibrometry Data

Smith, Ashley Nicole
http://rave.ohiolink.edu/etdc/view?acc_num=wright1421104791

Abstract Details

, Master of Science in Engineering (MSEgr), Wright State University, Electrical Engineering.
Laser vibrometry provides a method to identify running vehicles’ unique signatures using non-contact measurements. A vehicle’s engine, size, materials, shape, and other attributes affect its vibration signature. To develop the capability to classify and identify these signatures, a robust aided target recognition (AiTR) end-to-end process is evaluated and expanded. The main challenge in classifying a vehicle’s vibration signatures is presented by the operating conditions and parameters that vary as a function of sensor, environment, and collection locations on the target, among others. Some of the parameters affecting the vibration signatures include weather, terrain, sensor location, sensor type, and engine speed. Another challenge in vehicle classification is the determination of signal features that can overcome the differences created by these varying operating conditions. The end-to-end process consists of signal preprocessing, feature extraction, feature selection, classification, and identification. A total of 11 features from automatic speech recognition, seismology, and structural analysis and previously utilized in vibration exploration were used in this end-to-end process. Features were selected by two feature selection methods to determine the best feature set for vehicle classification. Finally, four classifiers were used to identify the vehicles’ signatures. Confusion matrices were used as metrics to evaluate the effectiveness of the end-to-end process. The entire process was tested on two sets of data: a military vehicle collection using accelerometers and a civilian vehicle collection using a laser vibrometer and accelerometers.
Arnab Shaw, Ph.D. (Advisor)
Brian Rigling, Ph.D. (Committee Member)
Fred Garber, Ph.D. (Committee Member)
85 p.
Electrical Engineering
vibrometry; classification; accelerometers; target recognition

Recommended Citations

Citations

  • Smith, A. N. (2014). End-to-End Classification Process for the Exploitation of Vibrometry Data [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1421104791

    APA Style (7th edition)

  • Smith, Ashley. End-to-End Classification Process for the Exploitation of Vibrometry Data. 2014. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1421104791.

    MLA Style (8th edition)

  • Smith, Ashley. "End-to-End Classification Process for the Exploitation of Vibrometry Data." Master's thesis, Wright State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1421104791

    Chicago Manual of Style (17th edition)

wright1421104791
624
© 2014, some rights reserved.Creative Commons License End-to-End Classification Process for the Exploitation of Vibrometry Data by Ashley Nicole Smith is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.