Data-modeling-enabled guidance, navigation, and control to enhance the lethality of interceptors against maneuvering targets

Holger M. Jaenisch; James Handley; Jeffrey P. Faucheux; Ken Lamkin

doi:10.1117/12.603413

28 March 2005 Data-modeling-enabled guidance, navigation, and control to enhance the lethality of interceptors against maneuvering targets

Holger M. Jaenisch, James Handley, Jeffrey P. Faucheux, Ken Lamkin

Author Affiliations +

Proceedings Volume 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005; (2005) https://doi.org/10.1117/12.603413
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

GNC algorithms are evolved from linear optimal control theory. This approach accommodates simple target maneuvers; however, these approaches lack robustness when advanced threats with intelligent target maneuvers are encountered. Kalman filters (KF) and Extended Kalman filters (EKF) require a priori defined models or equations of motion for objects being observed. Data Modeling autonomously assesses physical characteristics of a tracked object from only its measured motion. Estimates of object's mass, equivalent area, and probable control feedback loop parameters are obtained. These equations become state and process models for Kalman filters.

Citation Download Citation

Holger M. Jaenisch, James Handley, Jeffrey P. Faucheux, and Ken Lamkin "Data-modeling-enabled guidance, navigation, and control to enhance the lethality of interceptors against maneuvering targets", Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); https://doi.org/10.1117/12.603413

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