Optimal guidance and nonlinear estimation for interception of accelerating targets

Optimal guidance and nonlinear estimation algorithms are formulated for interception of an accelerating target vehicle during boost. For an interceptor with two-axis control of translational acceleration, time to go may be selected to null the component of commanded acceleration along the uncontroll...

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Veröffentlicht in:Journal of guidance, control, and dynamics control, and dynamics, 1995-09, Vol.18 (5), p.959-968
1. Verfasser: Hough, Michael E
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container_title Journal of guidance, control, and dynamics
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creator Hough, Michael E
description Optimal guidance and nonlinear estimation algorithms are formulated for interception of an accelerating target vehicle during boost. For an interceptor with two-axis control of translational acceleration, time to go may be selected to null the component of commanded acceleration along the uncontrolled axis. A nine-state, extended Kalman filter is formulated, in a Cartesian inertial frame. The filter dynamics model includes a vector-differential equation for the thrust acceleration vector of the target during a gravity-turn maneuver. With angle measurements from a strapdown seeker, very small miss distances can be achieved, despite large estimation errors in range, because of the time-to-go algorithm. Monte Carlo simulations are used to generate theoretical collision probabilities as functions of sensor measurement accuracy and filter update rate. (Author)
doi_str_mv 10.2514/3.21491
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subjects Acceleration control
Accuracy
Algorithms
Angle measurement
Computer simulation
Differential equations
Estimation
Interception
Kalman filtering
Mathematical models
Monte Carlo methods
Monte Carlo simulation
Optimal control systems
Probability
Vectors
Velocity
title Optimal guidance and nonlinear estimation for interception of accelerating targets
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