Patricia L Ryan

US Patent:

20210072348, Mar 11, 2021

Filed:

Sep 14, 2020

Appl. No.:

17/019531

Inventors:

John A. Malas - Kettering OH, US
Patricia A. Ryan - Centerville OH, US
John A. Cortese - Reading MA, US

Assignee:

US Gov't as represented by Secretary of Air Force - Wright-Patterson AFB OH

International Classification:

G01S 7/38
G01S 7/02
G06K 9/00
G06K 9/62

Abstract:

Methods are provided for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. Models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.

US Patent:

20200074339, Mar 5, 2020

Filed:

Oct 29, 2019

Appl. No.:

16/666516

Inventors:

John A. Malas - Kettering OH, US
Patricia A. Ryan - Centerville OH, US
John A. Cortese - Reading MA, US

Assignee:

U.S. Government as represented by Secretary of the Air Force - Wright-Patterson AFB OH

International Classification:

G06N 7/08
G06N 7/00
G06N 7/02

Abstract:

The present disclosure includes theoretical models and methods for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. These models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.