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{ "item_title" : "Vision-Aided Autonomous Precision Weapon Terminal Guidance Using a Tightly-Coupled INS and Predictive Rendering Techniques", "item_author" : [" Jonathan W. Beich "], "item_description" : "This thesis documents the development of the Vision-Aided Navigation using Statistical Predictive Rendering (VANSPR) algorithm which seeks to enhance the endgame navigation solution possible by inertial measurements alone. The eventual goal is a precision weapon that does not rely on GPS, functions autonomously, thrives in complex 3-D environments, and is impervious to jamming. The predictive rendering is performed by viewpoint manipulation of computer-generated of target objects. A navigation solution is determined by an Unscented Kalman Filter (UKF) which corrects positional errors by comparing camera images with a collection of statistically significant virtual images.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/02/509/642/1025096428_b.jpg", "price_data" : { "retail_price" : "18.95", "online_price" : "18.95", "our_price" : "18.95", "club_price" : "18.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Vision-Aided Autonomous Precision Weapon Terminal Guidance Using a Tightly-Coupled INS and Predictive Rendering Techniques|Jonathan W. Beich

Vision-Aided Autonomous Precision Weapon Terminal Guidance Using a Tightly-Coupled INS and Predictive Rendering Techniques

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Overview

This thesis documents the development of the Vision-Aided Navigation using Statistical Predictive Rendering (VANSPR) algorithm which seeks to enhance the endgame navigation solution possible by inertial measurements alone. The eventual goal is a precision weapon that does not rely on GPS, functions autonomously, thrives in complex 3-D environments, and is impervious to jamming. The predictive rendering is performed by viewpoint manipulation of computer-generated of target objects. A navigation solution is determined by an Unscented Kalman Filter (UKF) which corrects positional errors by comparing camera images with a collection of statistically significant virtual images.

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.

This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.

As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.


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Details

  • ISBN-13: 9781025096421
  • ISBN-10: 1025096428
  • Publisher: Hutson Street Press
  • Publish Date: May 2025
  • Dimensions: 9.21 x 6.14 x 0.42 inches
  • Shipping Weight: 0.63 pounds
  • Page Count: 200

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