Technology development has increased exponentially. Program managers are pushed to accelerate development. There are many resources available to program managers that enable acceleration, such as: additional resources in the form of funding, people and technology. There are also negative impacts to acceleration, such as: inclusion, inexperience program managers, and communication. This research seeks to identify the limit to which a program or project can be accelerated before the program manager begins to accept an unacceptable amount of pre-determined risk.
This research will utilize estimation algorithms used by sensor systems to estimate the current and future state of objects in space. The most common estimation algorithm used is the Kalman filter developed by Kalman (Bar-Shalom, Rong Li, & Kirubarajan, 2001). This research will examine the use of two Kalman filters in for the form of an Interacting Multiple Model (IMM) in order to predict the future state of the program. Traditional multiple model filters use Bayesian technique to adaptively switch between different motion models implemented in the filter structure (USA Patent No. 7030809, 2005). These logic designs rely upon a predefined Markov Switching Matrix (MSM). If the future state approaches a predetermined acceptable level of risk, the MSM will indicate to the program manager that the project has potentially reached a level of unacceptable risk.
","abstract_html":"<p>Technology development has increased exponentially. Program managers are pushed to accelerate development. There are many resources available to program managers that enable acceleration, such as: additional resources in the form of funding, people and technology. There are also negative impacts to acceleration, such as: inclusion, inexperience program managers, and communication. This research seeks to identify the limit to which a program or project can be accelerated before the program manager begins to accept an unacceptable amount of pre-determined risk.</p> <p>This research will utilize estimation algorithms used by sensor systems to estimate the current and future state of objects in space. The most common estimation algorithm used is the Kalman filter developed by Kalman (Bar-Shalom, Rong Li, & Kirubarajan, 2001). This research will examine the use of two Kalman filters in for the form of an Interacting Multiple Model (IMM) in order to predict the future state of the program. Traditional multiple model filters use Bayesian technique to adaptively switch between different motion models implemented in the filter structure (USA Patent No. 7030809, 2005). These logic designs rely upon a predefined Markov Switching Matrix (MSM). If the future state approaches a predetermined acceptable level of risk, the MSM will indicate to the program manager that the project has potentially reached a level of unacceptable risk.</p>","abstract_has_math":false,"creators":["Smith-Carroll, Amy Sunshine"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Engineering Management & Systems Engineering","degree_department":null,"school":null,"contributors":["Andres Sousa-Poza","Charles Keating","Adrian Gheorghe","Victor Gehman"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-12-01T08:00:00Z","date_published":"2020-12-01T08:00:00Z","updated_at":"2026-07-24T03:35:38Z","subjects":["Accelerated acquisition","Interacting multiple model","Kalman filter","Program acceleration","Program risk","Electrical and Computer Engineering","Operational Research","Risk Analysis","Systems Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9798557048804"],"render_values":[{"text":"9798557048804","href":null,"code":true}]}]},"links":{"outbound_url":"https://digitalcommons.odu.edu/emse_etds/181","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Andres Sousa-Poza","Charles Keating","Adrian Gheorghe","Victor Gehman"]},{"key":"dc:creator","label":"Author","values":["Smith-Carroll, Amy Sunshine"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2021-01-05T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Management & Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Accelerated acquisition","Interacting multiple model","Kalman filter","Program acceleration","Program risk","Electrical and Computer Engineering","Operational Research","Risk Analysis","Systems Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9798557048804","https://digitalcommons.odu.edu/emse_etds/181"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Technology development has increased exponentially. Program managers are pushed to accelerate development. There are many resources available to program managers that enable acceleration, such as: additional resources in the form of funding, people and technology. There are also negative impacts to acceleration, such as: inclusion, inexperience program managers, and communication. This research seeks to identify the limit to which a program or project can be accelerated before the program manager begins to accept an unacceptable amount of pre-determined risk.
This research will utilize estimation algorithms used by sensor systems to estimate the current and future state of objects in space. The most common estimation algorithm used is the Kalman filter developed by Kalman (Bar-Shalom, Rong Li, & Kirubarajan, 2001). This research will examine the use of two Kalman filters in for the form of an Interacting Multiple Model (IMM) in order to predict the future state of the program. Traditional multiple model filters use Bayesian technique to adaptively switch between different motion models implemented in the filter structure (USA Patent No. 7030809, 2005). These logic designs rely upon a predefined Markov Switching Matrix (MSM). If the future state approaches a predetermined acceptable level of risk, the MSM will indicate to the program manager that the project has potentially reached a level of unacceptable risk.
"]},{"key":"dc:title","label":"Title","values":["Using Interacting Multiple Model Filters to Indicate Program Risk"]}]}],"canonical_facts":{"dc:contributor":["Andres Sousa-Poza","Charles Keating","Adrian Gheorghe","Victor Gehman"],"dc:creator":["Smith-Carroll, Amy Sunshine"],"dc:date.available":["2021-01-05T08:00:00Z"],"dc:description.abstract":["Technology development has increased exponentially. Program managers are pushed to accelerate development. There are many resources available to program managers that enable acceleration, such as: additional resources in the form of funding, people and technology. There are also negative impacts to acceleration, such as: inclusion, inexperience program managers, and communication. This research seeks to identify the limit to which a program or project can be accelerated before the program manager begins to accept an unacceptable amount of pre-determined risk.
This research will utilize estimation algorithms used by sensor systems to estimate the current and future state of objects in space. The most common estimation algorithm used is the Kalman filter developed by Kalman (Bar-Shalom, Rong Li, & Kirubarajan, 2001). This research will examine the use of two Kalman filters in for the form of an Interacting Multiple Model (IMM) in order to predict the future state of the program. Traditional multiple model filters use Bayesian technique to adaptively switch between different motion models implemented in the filter structure (USA Patent No. 7030809, 2005). These logic designs rely upon a predefined Markov Switching Matrix (MSM). If the future state approaches a predetermined acceptable level of risk, the MSM will indicate to the program manager that the project has potentially reached a level of unacceptable risk.
"],"dc:identifier":["9798557048804","https://digitalcommons.odu.edu/emse_etds/181"],"dc:subject":["Accelerated acquisition","Interacting multiple model","Kalman filter","Program acceleration","Program risk","Electrical and Computer Engineering","Operational Research","Risk Analysis","Systems Engineering"],"dc:title":["Using Interacting Multiple Model Filters to Indicate Program Risk"],"thesis:degree_discipline":["Engineering Management & Systems Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:35:38Z"}