Time filter

Source Type

Rolla, MO, United States

Ohlmeyer E.J.,Aerospace Science Applications | Balakrishnan S.N.,IST-Rolla
AIAA Guidance, Navigation, and Control Conference

This paper describes a series of system design and performance studies for a notional air-launched interceptor intended for boost phase interception of a ballistic missile target. A candidate launch platform for the interceptor is an unmanned air vehicle loitering in airspace near the target launch site. A notional target from the open literature is used for analysis. The conceptual interceptor is designed from first principles using standard methods. Designs for two target tracking filters based on the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are developed. Then tracking accuracy is quantified as a function of measurement noise levels. The effects of tracking errors and prediction interval on errors in the Predicted Intercept Point (PIP) is determined. Then an evaluation of how the PIP errors affect interceptor terminal performance is conducted, using heading errors and line-of-sight pointing errors as metrics. The performance of the candidate system design is then assessed. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Source

Agency: Department of Defense | Branch: Missile Defense Agency | Program: SBIR | Phase: Phase I | Award Amount: 100.00K | Year: 2006

A recently developed nonlinear controller called theta-D and the Higher Order Sliding Mode (HOSM) control are the centerpiece around which an Integrated Guidance and Control (IGC) scheme and a nonlinear filter technique are built to enhance the lethality of hit-to-kill interceptors and offer a means to increase the maneuver ratio advantage. This proposal includes the development of a 6DOF based IGC scheme that combines the guidance and control objectives into a single framework. Nonlinear filters utilizing theta-D and HOSM are proposed in order to improve the accuracy of the target maneuver estimates. Furthermore, a novel filter based on cost function formulation and a guidance law designed to enhance system observability are to be incorporated into the IGC architecture, and various spiraling and weaving target scenarios will be used to evaluate the proposed algorithms. The outcome of this research is expected to produce sound IGC designs, based on two theoretically rigorous techniques, that substantially decrease miss distance in hit-to-kill interceptors.

Agency: Department of Defense | Branch: Missile Defense Agency | Program: SBIR | Phase: Phase II | Award Amount: 749.97K | Year: 2007

This proposal continues on the successful Integrated Guidance and Control techniques developed through the Phase I effort. A new VIGC formulation has been shown to yield good preliminary results. Research during Phase II will develop IGC algorithms with an optimal approach, with an inner loop-outer loop appraoach and and a heading error approach. IST-Rolla also will develop nonlinear filters to make the simulations more realistic for high-speed target interceptor scenarios. The combinations of the optimal theta-D control and cost based and theta-D filter are expected to yield near zero miss distances.

Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 598.97K | Year: 2011

IST-Rolla developed two nonlinear filters for spacecraft orbit determination during the Phase I contract. The theta-D filter and the cost based filter, CBF, were developed and used in various orbit determination scenarios. The scenarios were application to low Earth orbit, range only, and range and range rate estimation. The modified state observer was also developed to estimate uncertainty in the dynamic model besides estimation of orbital states.Phase I research showed that there is a problem with the linear-like form that is used by many nonlinear filters such as the State Dependant Riccati Equation filter (SDRE filter), and the theta-D and CBF. A study of the observability led to important discoveries about the lack of observabilty in some formulations. Detailed study of the working of the proposed nonlinear filters in terms of observability and their application to more precise orbit determination and model uncertainty estimation will be undertaken in Phase II.Also learned from Phase I, IST-Rolla will focus more on how and where these nonlinear filters can help NASA. There will be three main applications studied during Phase II: interplanetary orbit determination, space debris tracking, and interplanetary landing spacecraft tracking. These applications were chosen because of their relevance to current NASA missions and the nonlinearity of the measurements involved should show the need for the nonlinear filters. Furthermore, working algorithms and software will be given to NASA to test on ongoing applications.

Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 99.45K | Year: 2010

Spacecraft need accurate position and velocity estimates in order to control their orbits. Some missions require more accurate estimates than others, but nearly all missions need some type of orbit determination. IST-Rolla seeks to provide highly accurate algorithms that do not overpower the spacecraft's computer. Many new, powerful algorithms exist such as the particle filter and the unscented Kalman filter, but most of them involve integrating several state vectors, and those integrations devour the computing power available. IST-Rolla will implement the è-D technique, the cost based filter (CBF), and the neural network estimator for orbit determination(developed by IST-Rolla Engineers) and analyze the results. These filters are nonlinear and might provide better accuracy than the extended Kalman filter (EKF) which is widely used, without being computationally cumbersome as the particle filter and unscented Kalman filter. The theta-D technique approximates the solution to the filter-related Ricatti Equation. The CBF is an attempt to formulation of the filter under an 'optimal' framework. The neural network estimator works to estimate the modeling errors online so that the estimates become more accurate.

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