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Modified linear theory and Kalman filtering for in flight projectile impact point prediction

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dc.contributor.advisor Costello, Mark F.
dc.creator Hainz, Leonard C.
dc.date.accessioned 2011-08-10T23:17:01Z
dc.date.available 2011-08-10T23:17:01Z
dc.date.copyright 2004-08-20
dc.date.issued 2004-08-20
dc.identifier.uri http://hdl.handle.net/1957/22515
dc.description Graduation date: 2005 en_US
dc.description.abstract A method for real time in-flight prediction of the ground impact point of an indirect fire projectile is investigated. The method investigated is comprised of a combination of an impact point predictor and an extended Kalman filter state estimator based on modified linear theory. The modified linear theory model is formed through a re-derivation of projectile linear theory with a less restrictive set of assumptions. Performance documentation for the modified linear theory model is provided in the form of typical results for both a short range trajectory of a direct fire fin stabilized projectile and a long range trajectory for an indirect fire spin stabilized round. The extended Kalman filter blends sensor data with an internal modified linear theory model to obtain an estimate of the projectile state. Three sensor configurations are explored, each assuming that sensor measurements are available at discrete times during flight. Results generated from a previously validated non-linear six degree of freedom projectile model and simulated noisy sensor readings indicate the technique is capable of predicting ground impact to within 15 meters at the apex of the trajectory when full state feedback is available. en_US
dc.language.iso en_US en_US
dc.subject.lcsh Projectiles, Aerial -- Aerodynamics en_US
dc.subject.lcsh Target acquisition en_US
dc.subject.lcsh Kalman filtering en_US
dc.title Modified linear theory and Kalman filtering for in flight projectile impact point prediction en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Master of Science (M.S.) in Mechanical Engineering en_US
dc.degree.level Master's en_US
dc.degree.discipline Engineering en_US
dc.degree.grantor Oregon State University en_US
dc.description.digitization File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0.82 on a Canon DR-9080C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR. en_US
dc.description.peerreview no en_us


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