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[{"key": "dc.contributor.advisor", "value": "Raita-Hakola, Anna-Maria", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Joutsalainen, Jukka", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2024-12-05T19:57:50Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2024-12-05T19:57:50Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2024", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/98841", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Per\u00e4siipi-ohjattujen ohjusten itse hakeutuva homing-ohjaus on laajasti tutkittu aihe, mutta tutkimuksessa on merkitt\u00e4v\u00e4 aukko, mik\u00e4 liittyy kuvantunnistusmenetelmien integrointiin kamera-hakup\u00e4ihin perustuvien ohjausj\u00e4rjestelmien osaksi. Erityisesti teoria liittyen hy\u00f6dyllisen kohde-tiedon tuottaminen on v\u00e4h\u00e4ist\u00e4 julkisen tutkimuksen osalta. \n\nT\u00e4m\u00e4 tutkimus ehdottaa ongelman ratkaisuksi neuroverkkopohjaista kohteen tunnistusta ja joukkoa eri\u00e4vi\u00e4 laskentastrategioiden sarjoja, joiden avulla voidaan tuottaa tietoa ohjauslain ja ohjausteorioiden tueksi. Vaikka neuroverkkojen kehitys ja niiden onnistuneet sovellukset kuvantunnistuksessa ovatkin edistyneet, nykyiset avoimesti julkaistut tutkimukset itse hakeutuvista ohjuksista eiv\u00e4t ole mainittavista tutkineet objektitunnistuksen integroimista visuaaliseen homing-ohjaukseen. T\u00e4m\u00e4 puute merkitt\u00e4v\u00e4, sill\u00e4 neuroverkkot ovat jo todentaneet potentiaalinsa monissa kuvantunnistuksen sovelluksissa. Lis\u00e4ten, yleiset ohjusten ohjauksen periaatteet, kuten inertiaalinen navigointi ja n\u00e4k\u00f6yhteyteen perustava ohjaus, ovat laajasti k\u00e4yt\u00f6ss\u00e4 my\u00f6s ilmailu- ja avaruusj\u00e4rjestelmiss\u00e4, korostaen konseptin yleistett\u00e4vyytt\u00e4 ja potentiaalia. \n\nT\u00e4m\u00e4 tutkimus tarjoaa kattavan joukon ratkaisuja neuroverkko pohjaisen visuaalisen ohjauksen toteuttamiseksi X-siipi konfiguraatiota k\u00e4ytt\u00e4viss\u00e4 ohjuksissa. N\u00e4m\u00e4 ratkaisut kattavat loogisen kulun aina kohteen tunnistamisesta vaiheeseen, jossa fysikaalisiin teorioihin pohjautuvat autopilot tuottavat ohjausekomentoja. N\u00e4m\u00e4 ratkaisut pyrkiv\u00e4t t\u00e4ytt\u00e4m\u00e4\u00e4n valittujen ohjauslakien ja teorioiden vaatimukset ja varmistamaan onnistuneen ohjauksen. Tutkimus tarjoaa my\u00f6s perusperiaatteet ylitt\u00e4vi\u00e4 ratkaisuja jatkokehityksen tueksi ja teorian laajentamiseksi. \n\nK\u00e4siteltyjen teorioiden laajuus huomioon ottaen, useita alueita j\u00e4\u00e4 avoimiksi tulevaisuuden tutkimukselle. N\u00e4ihin kuuluu mm. ehdotettujen menetelmien tarkkuuden arviointi ja konseptin testaaminen simuloiduissa ymp\u00e4rist\u00f6iss\u00e4 oikeaan laitteistoon perustuen. Lis\u00e4ksi konseptin haasteisiin liittyy kehittyneiden, mutta keveiden lentotietokoneiden kehitt\u00e4minen, jotta esitettyjen teorioidaan laskennalliset vaatimukset voitaisiin t\u00e4ytt\u00e4\u00e4 suurin nopeuksin.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Homing guidance for tail-controlled missiles is a well-researched topic, but there is a notable gap in the literature regarding the integration of image recognition methods for guidance systems based on Television Camera (TV)-seeker. Specifically, deriving useful target information from the image data captured by a TV-seeker remains a challenge. This research addresses this issue by proposing use of neural network-based object detection for target localization and classification, which would create base for set of computational strategies to further derive refine information for guidance efforts. \n\nDespite the advancements in neural networks and their successful applications in the field of image recognition, current openly published research on homing missiles has not explored the integration of object detection systems into visual homing guidance. This gap is particularly significant, given the potential for neural networks to enhance guidance systems. The general principles used in missile guidance, such as inertial navigation and line-of-sight-based guidance, are also extensively applied in aerial and space-borne systems, emphasizing the generalization capability and potential of the concept. \n\nThis research offers a comprehensive set of solutions for implementing neural network-based visual guidance in X-tail controlled missiles, detailing the process from target detection to the application of autopilot commands based on physical reasoning. These solutions aim to achieve the requirements of the selected guidance laws, ultimately ensuring successful engagement. Furthermore, additional logic is introduced to extend the basic theories, laying the groundwork for future developments. \n\nGiven the scope of the concepts discussed, several areas remain open for future research. These include evaluating the accuracy of the proposed methods, testing the concept in simulated environments with real hardware, and developing more advanced flight computers capable of handling the computational demands of such systems.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2024-12-05T19:57:50Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2024-12-05T19:57:50Z (GMT). No. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "83", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Integration of Object Detecting Neural Network-Based Strapdown-Seeker in Terminal Phase Visual Guidance for X-Tail-Controlled Missiles", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202412057656", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Specialisation in Software Development", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Ohjelmistokehityksen opintosuunta", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.copyright", "value": "\u00a9 The Author(s)", "language": null, "element": "rights", "qualifier": "copyright", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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