Investigation of the use of acceleration estimates by endgame guidance laws

Looze, Douglas P.; Hsu, John Y.; Grunberg, Daniel

doi:10.2514/3.20537

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The maximum achievable accuracy of the proposed estimator for non-linear problems is obtained by the Cramer-Rao lower bound (CRLB) [17,18] analytically. CRLB is used in simulation to compare the EKF and NRLS accuracies with a lower bound on the error covariance that can be achieved by any unbiased estimate for a non-linear stochastic system in terms of the Fisher information matrix.…”

Section: Thrust Acceleration Estimation Using An On-line Non-linear Rmentioning

confidence: 99%

Thrust acceleration estimation using an on-line non-linear recursive least squares algorithm

Ghahramani

Naghash

Towhidkhah

2008

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this article, a robust non-linear recursive algorithm, featuring a highly reduced computational load, is proposed to estimate the thrust acceleration of a typical flight vehicle. The robustness of this new algorithm allows a significant increase of deviations in initial values of the estimated parameters. In the proposed algorithm, at first, a transformation of the nonlinear measurement equation to a linear one, without any approximation, is obtained. Then the recursive least squares algorithm is applied to the transformed equation. The maximum achievable accuracy of the estimation for the non-linear problem is obtained analytically by the Cramer-Rao lower bound and is compared with simulation results. Extensive simulations showed that the new method provides an unbiased as well as a more robust thrust acceleration estimate in comparison with the extended Kalman filter. Moreover, the proposed method is beneficial in that it has a lower number of parameters and results in a simple design with less computational effort.

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Section: Thrust Acceleration Estimation Using An On-line Non-linear Rmentioning

confidence: 99%

Thrust acceleration estimation using an on-line non-linear recursive least squares algorithm

Ghahramani

Naghash

Towhidkhah

2008

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Kim et al 2 employ a kinematic algorithm to predict collision courses. Looze et al 3 analyze use of target acceleration estimates in the end game. Cochran et al 4 obtain analytical solutions to the nonlinear equations of relative motion for a constant-velocity target and an interceptor steered via a form of proportional navigation.…”

Section: Introductionmentioning

confidence: 99%

Reduced-computation end-game steering laws for predictive guidance

Barron

1995

Journal of Guidance, Control, and Dynamics

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A reduced-computation end-game (RCEG) steering law is derived for predictive guidance and simulated for a class of command guided weapons. The RCEG law considers maneuver control system response dynamics and variable axial and normal accelerations. It substantially reduces the guidance computational burden, while achieving accuracy comparable to that realizable with iterative predictive guidance (IPG). With IPG, each iteration requires numerical integration of vehicle equations of motion to predict the miss distance, whereas the RCEG law is noniterative and requires no integration. RCEG guidance uses closed-form miss distance estimates based upon lumped-parameter rotational and translational dynamics. Results of a six-degree-of-freedom simulation of a hypersonic intercept weapon system indicate that the RCEG law, compared with a representative form of IPG, has miss distances that are statistically similar to those of IPG but a computational burden smaller by a factor of approximately 36.

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Investigation of the use of acceleration estimates by endgame guidance laws

Cited by 2 publications

References 8 publications

Thrust acceleration estimation using an on-line non-linear recursive least squares algorithm

Thrust acceleration estimation using an on-line non-linear recursive least squares algorithm

Reduced-computation end-game steering laws for predictive guidance

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