Since the 1990’s, there has been increased focus on creating navigation systems for small unmanned systems, particularly small unmanned aerial systems (SUAS). Due to size, weight, and cost restrictions, compared to larger more costly manned systems, navigation systems for SUAS have evolved to be quite different from the proven systems of the past. Today, there are many solutions for the problem of navigation for SUAS. The problem has now become choosing the most fitting navigation solution for a given application/mission. This is particularly true for evaluating solutions that are fundamentally different.
This research analyses the performance and sensitivity of four sensor fusion solutions for attitude estimation under multiple simulated flight conditions. There are three different hardware configurations between the four estimators. For this reason, each estimator is tuned to be experimentally optimal, as to provide a fair comparison between different estimators. With each estimator tuned to its highest performance, the estimators are compared based on their sensitivity to tuning error, sensor bias, and estimator initialization error. Finally the estimators' accuracy performances are directly compared.
This thesis also provides methods to tune different configuration estimators to their individual best performances. These methods show that choosing tuning parameters based on sensor noise covariance, as is typically done in research, does not produce optimal performance for all estimator formulations. After comparing multiple sensitivity and performance properties of the estimators, observations are provided regarding the efficacy of the analyses, including the applicability of the metrics used to determine performance. Some metrics where shown to be misleading for particular estimators or analyses. Ultimately, guidance is given for choosing performance metrics capable of comparing different solutions.
","abstract_html":"<p>Since the 1990’s, there has been increased focus on creating navigation systems for small unmanned systems, particularly small unmanned aerial systems (SUAS). Due to size, weight, and cost restrictions, compared to larger more costly manned systems, navigation systems for SUAS have evolved to be quite different from the proven systems of the past. Today, there are many solutions for the problem of navigation for SUAS. The problem has now become choosing the most fitting navigation solution for a given application/mission. This is particularly true for evaluating solutions that are fundamentally different.</p> <p>This research analyses the performance and sensitivity of four sensor fusion solutions for attitude estimation under multiple simulated flight conditions. There are three different hardware configurations between the four estimators. For this reason, each estimator is tuned to be experimentally optimal, as to provide a fair comparison between different estimators. With each estimator tuned to its highest performance, the estimators are compared based on their sensitivity to tuning error, sensor bias, and estimator initialization error. Finally the estimators' accuracy performances are directly compared.</p> <p>This thesis also provides methods to tune different configuration estimators to their individual best performances. These methods show that choosing tuning parameters based on sensor noise covariance, as is typically done in research, does not produce optimal performance for all estimator formulations. After comparing multiple sensitivity and performance properties of the estimators, observations are provided regarding the efficacy of the analyses, including the applicability of the metrics used to determine performance. Some metrics where shown to be misleading for particular estimators or analyses. Ultimately, guidance is given for choosing performance metrics capable of comparing different solutions.</p>","abstract_has_math":false,"creators":["Hoffman, Ryan W."],"institution":null,"degree_name":"Master of Science in Mechanical Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-07-01T07:00:00Z","date_published":"2018-07-01T07:00:00Z","updated_at":"2026-07-27T19:25:37Z","subjects":["attitude","sensor fusion","MEMS","Mechanical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/407","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Hoffman, Ryan W."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["attitude","sensor fusion","MEMS","Mechanical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/407"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Since the 1990’s, there has been increased focus on creating navigation systems for small unmanned systems, particularly small unmanned aerial systems (SUAS). Due to size, weight, and cost restrictions, compared to larger more costly manned systems, navigation systems for SUAS have evolved to be quite different from the proven systems of the past. Today, there are many solutions for the problem of navigation for SUAS. The problem has now become choosing the most fitting navigation solution for a given application/mission. This is particularly true for evaluating solutions that are fundamentally different.
This research analyses the performance and sensitivity of four sensor fusion solutions for attitude estimation under multiple simulated flight conditions. There are three different hardware configurations between the four estimators. For this reason, each estimator is tuned to be experimentally optimal, as to provide a fair comparison between different estimators. With each estimator tuned to its highest performance, the estimators are compared based on their sensitivity to tuning error, sensor bias, and estimator initialization error. Finally the estimators' accuracy performances are directly compared.
This thesis also provides methods to tune different configuration estimators to their individual best performances. These methods show that choosing tuning parameters based on sensor noise covariance, as is typically done in research, does not produce optimal performance for all estimator formulations. After comparing multiple sensitivity and performance properties of the estimators, observations are provided regarding the efficacy of the analyses, including the applicability of the metrics used to determine performance. Some metrics where shown to be misleading for particular estimators or analyses. Ultimately, guidance is given for choosing performance metrics capable of comparing different solutions.
"]},{"key":"dc:title","label":"Title","values":["Sensitivity Analyses of Optimized Attitude Estimators Using Sensor Fusion Solutions for Low-Cost MEMS Configurations"]}]}],"canonical_facts":{"dc:creator":["Hoffman, Ryan W."],"dc:description.abstract":["Since the 1990’s, there has been increased focus on creating navigation systems for small unmanned systems, particularly small unmanned aerial systems (SUAS). Due to size, weight, and cost restrictions, compared to larger more costly manned systems, navigation systems for SUAS have evolved to be quite different from the proven systems of the past. Today, there are many solutions for the problem of navigation for SUAS. The problem has now become choosing the most fitting navigation solution for a given application/mission. This is particularly true for evaluating solutions that are fundamentally different.
This research analyses the performance and sensitivity of four sensor fusion solutions for attitude estimation under multiple simulated flight conditions. There are three different hardware configurations between the four estimators. For this reason, each estimator is tuned to be experimentally optimal, as to provide a fair comparison between different estimators. With each estimator tuned to its highest performance, the estimators are compared based on their sensitivity to tuning error, sensor bias, and estimator initialization error. Finally the estimators' accuracy performances are directly compared.
This thesis also provides methods to tune different configuration estimators to their individual best performances. These methods show that choosing tuning parameters based on sensor noise covariance, as is typically done in research, does not produce optimal performance for all estimator formulations. After comparing multiple sensitivity and performance properties of the estimators, observations are provided regarding the efficacy of the analyses, including the applicability of the metrics used to determine performance. Some metrics where shown to be misleading for particular estimators or analyses. Ultimately, guidance is given for choosing performance metrics capable of comparing different solutions.
"],"dc:identifier":["https://commons.erau.edu/edt/407"],"dc:subject":["attitude","sensor fusion","MEMS","Mechanical Engineering"],"dc:title":["Sensitivity Analyses of Optimized Attitude Estimators Using Sensor Fusion Solutions for Low-Cost MEMS Configurations"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-27T19:25:37Z"}