Radar Cross Section Constraints in Flight Path Optimization

Norsell, Martin

doi:10.2514/1.16

Cited by 2 publications

(9 citation statements)

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“…15,16 A more general formulation in three dimensions was developed using a continuous and differentiable RCS representation. 17 Similar work has been performed by Misovec et al 18 This method works well for relatively short total flight distances, that is, in the 50-100 km range. For large distances many time steps are needed to resolve the rigid-body dynamics of the aircraft, which results in very large optimization problems.…”

Section: Introductionsupporting

confidence: 68%

Multistage Trajectory Optimization with Radar Range Constraints

Norsell

2005

Journal of Aircraft

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The problem of finding an optimal aircraft trajectory for long-distance flights in three dimensions subject to radar detection constraints is considered. A general point-mass model previously developed is not suitable because the time discretization needs to be very fine to resolve the rigid-body dynamics resulting in very large optimization problems. Different reduced mathematical models are derived and compared to the more general performance model. Finally, a long-range mission involving two subsonic jet trainers approaching a radar target is numerically simulated. The results indicate that the time a hostile radar has an approaching aircraft under surveillance can be greatly reduced by using the proposed methodology.

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Section: Introductionsupporting

confidence: 68%

Multistage Trajectory Optimization with Radar Range Constraints

Norsell

2005

Journal of Aircraft

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“…The optimization problem, defined by Eqs. (2) and (3), then can be stated in the following NLP form:…”

Section: Reformulationmentioning

confidence: 99%

“…Lower and upper bounds are represented by lb and ub, respectively. Equations (2) and (3) are standard in optimal control and are further explained in [15,18].…”

Section: Parameterizationmentioning

confidence: 99%

“…(2) and (3) in terms of the B-spline coefficients C i j to be solved by the sequential quadratic programming package NPSOL. This reformulation yields a nonlinear programming problem.…”

Section: Reformulationmentioning

confidence: 99%

“…We also develop temporal observability constraints for strategic detection avoidance. In [2], radar cross-section models are used to find paths with distinct periods for optimizing detection or time. Kim and Hespanha [3] developed anisotropic shortest-path methods for designing minimum risk flight paths.…”

Section: Nomenclaturementioning

confidence: 99%

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Framework for Low-Observable Trajectory Generation in Presence of Multiple Radars

Inanc

Muezzinoglu

Misovec

et al. 2008

Journal of Guidance, Control, and Dynamics

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This paper explores the problem of finding a real-time optimal trajectory for unmanned aerial vehicles to minimize their probability of detection by opponent multiple radar detection systems. The problem is handled using the nonlinear trajectory generation method developed by Milam et al. (Milam, M., Mushambi, K., and Murray, R., "New The paper presents a formulation of the trajectory generation task as an optimal control problem, where temporal constraints allow periods of high observability interspersed with periods of low observability. This feature can be used strategically to aid in avoiding detection by an opponent radar. The guidance is provided in the form of sampled tabular data. It is then shown that the success of nonlinear trajectory generation on the proposed low-observable trajectory generation problem depends upon an accurate parameterization of the guidance data. In particular, such an approximator is desired to have a compact architecture, a minimum number of design parameters, and a smooth continuously differentiable input-output mapping. Artificial neural networks as universal approximators are known to possess these features, and thus are considered here as appropriate candidates for this task. Comparison of artificial neural networks against B-spline approximators is provided, as well. Numerical simulations on multiple radar scenarios illustrate unmanned air vehicle trajectories optimized for both detectability and time. NomenclatureB j;k i t = B-spline basis function for the output z i C i j = coefficients of the B-splines e ac = aircraft positions along the east axis k i = degree of spline polynomial l i = number of knot intervals m i = number of smoothness conditions at knot points n ac = aircraft positions along the north axis p i = number of coefficients of each output r = slant range s"; = signature function u ac = aircraft positions along the up axis ut = control input xt = state of the system z = differentially flat output = azimuth angle " = elevation angle = heading angle

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Radar Cross Section Constraints in Flight Path Optimization

Cited by 2 publications

References 0 publications

Multistage Trajectory Optimization with Radar Range Constraints

Multistage Trajectory Optimization with Radar Range Constraints

Framework for Low-Observable Trajectory Generation in Presence of Multiple Radars

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