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Interception time and uncertainty optimization for tangent-impulse orbit interception problem
The traditional tangent impulse interception problem does not consider the influence of actual deviation. However, by taking the actual state deviation of the interceptor into the orbit design process, an interception orbit that is more robust than the nominal orbit can be obtained. Therefore, we st...
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Published in: | Defence technology 2022-03, Vol.18 (3), p.418-440 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The traditional tangent impulse interception problem does not consider the influence of actual deviation. However, by taking the actual state deviation of the interceptor into the orbit design process, an interception orbit that is more robust than the nominal orbit can be obtained. Therefore, we study the minimum time interception problem and the minimum terminal interception error problem under tangent impulse conditions and give an orbit optimization method that considers the interception time and the interception uncertainty. First, we express the interceptor's transfer time equation as a form of flight path angle, establish a global optimization model for solving the minimum time tangent impulse interception and give a hybrid optimization algorithm based on Augmented Lagrange Genetic Algorithm - Sequential Quadratic Programming (ALGA-SQP). Secondly, we use the universal time equation and Bootstrap resampling technology to calculate the interceptor's terminal error distribution and establish the relevant global optimization model by using the circumscribed cuboid volume of the interceptor's terminal position error ellipsoid as the optimization index. Finally, we combined the above two single-objective optimization models to establish a global multi-objective optimization model that considers interception time and interception uncertainty and gave a hybrid multi-objective optimization algorithm based on Non-dominated Sorting Genetic Algorithm II - Goal Achievement Method (NSGA2-GAM). The simulation example verifies the effectiveness of this method. |
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ISSN: | 2214-9147 2214-9147 |
DOI: | 10.1016/j.dt.2021.02.006 |