{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:osu1366033578"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:osu1366033578","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"PULSED RADAR TARGET RECOGNITION BASED ON MICRO-DOPPLER SIGNATURES USING WAVELET ANALYSIS","abstract":"Radar based automatic target recognition systems are commonly used in perimeter protection and surveillance applications. These systems determine the nature of a target moving in the radar's field of view using its echo signal. Such an echo signal contains the target's micro-Doppler (μ-D) signature as well as its macro-motion related parameters. This thesis presents and compares three different approaches to develop such a system to distinguish between humans, dogs and background clutter using a low power pulsed radar. Each of these approaches rely on one among three different joint time-frequency transforms such as the short-time Fourier transform, the wavelet packet transform and the Haar transform to extract key μ-D signature related features from the time-frequency plane representation of the echo signal. These μ-D signature based features are combined with relative range profile based ones that characterize the detected target's motion at a gross level. These features, extracted from a variety of field data, are used to train and test different classifiers that finally declare the type of the target. The comparative performances of these three methods have been discussed. The Haar transform based approach, in particular, seems to show promise for implementation on computationally constrained platforms like motes used in wireless sensor network applications.","abstract_html":"Radar based automatic target recognition systems are commonly used in perimeter protection and surveillance applications. These systems determine the nature of a target moving in the radar's field of view using its echo signal. Such an echo signal contains the target's micro-Doppler (μ-D) signature as well as its macro-motion related parameters. This thesis presents and compares three different approaches to develop such a system to distinguish between humans, dogs and background clutter using a low power pulsed radar. Each of these approaches rely on one among three different joint time-frequency transforms such as the short-time Fourier transform, the wavelet packet transform and the Haar transform to extract key μ-D signature related features from the time-frequency plane representation of the echo signal. These μ-D signature based features are combined with relative range profile based ones that characterize the detected target's motion at a gross level. These features, extracted from a variety of field data, are used to train and test different classifiers that finally declare the type of the target. The comparative performances of these three methods have been discussed. The Haar transform based approach, in particular, seems to show promise for implementation on computationally constrained platforms like motes used in wireless sensor network applications.","abstract_has_math":false,"creators":["Kizhakkel, Vinit Rajan"],"institution":"The Ohio State University","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Krishnamurthy, Ashok"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-07-23","date_published":"2013-07-23","updated_at":"2026-07-24T03:37:46Z","subjects":["Electrical Engineering","Computer Science","Engineering","radar","pulsed radar","target recognition","wavelet","micro-Doppler"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. 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Each of these approaches rely on one among three different joint time-frequency transforms such as the short-time Fourier transform, the wavelet packet transform and the Haar transform to extract key μ-D signature related features from the time-frequency plane representation of the echo signal. These μ-D signature based features are combined with relative range profile based ones that characterize the detected target's motion at a gross level. These features, extracted from a variety of field data, are used to train and test different classifiers that finally declare the type of the target. The comparative performances of these three methods have been discussed. The Haar transform based approach, in particular, seems to show promise for implementation on computationally constrained platforms like motes used in wireless sensor network applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.118","2.36 MB"]},{"key":"dc:title","label":"Title","values":["PULSED RADAR TARGET RECOGNITION BASED ON MICRO-DOPPLER SIGNATURES USING WAVELET ANALYSIS"]}]}],"canonical_facts":{"dc:contributor":["Krishnamurthy, Ashok"],"dc:creator":["Kizhakkel, Vinit Rajan"],"dc:date":["2013-07-23"],"dc:description":["Radar based automatic target recognition systems are commonly used in perimeter protection and surveillance applications. These systems determine the nature of a target moving in the radar's field of view using its echo signal. Such an echo signal contains the target's micro-Doppler (μ-D) signature as well as its macro-motion related parameters. This thesis presents and compares three different approaches to develop such a system to distinguish between humans, dogs and background clutter using a low power pulsed radar. Each of these approaches rely on one among three different joint time-frequency transforms such as the short-time Fourier transform, the wavelet packet transform and the Haar transform to extract key μ-D signature related features from the time-frequency plane representation of the echo signal. These μ-D signature based features are combined with relative range profile based ones that characterize the detected target's motion at a gross level. These features, extracted from a variety of field data, are used to train and test different classifiers that finally declare the type of the target. The comparative performances of these three methods have been discussed. The Haar transform based approach, in particular, seems to show promise for implementation on computationally constrained platforms like motes used in wireless sensor network applications."],"dc:format":["application/pdf","p.118","2.36 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=osu1366033578"],"dc:language":["English"],"dc:publisher":["The Ohio State University / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. 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