Using Interacting Multiple Model Filters to Indicate Program Risk

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

Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies

The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance