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• W3120437499 abstract "In this paper, a set-membership filter-based pure proportional navigation (PPN) law for missile guidance is proposed. A recursive set-membership filter for nonlinear dynamical systems subject to unknown but bounded process and measurement noises is constructed using the state dependent coefficient parameterization of the nonlinear dynamics. At every recursion, the filter requires solutions to two convex optimization problems (namely, semi-definite programs) and these solutions can be efficiently obtained. This filter is implemented to estimate the states of planar engagement between a missile and a target. The corresponding corrected state estimates are utilized to synthesize a PPN guidance law for the missile. Detailed simulations are carried out for stationary, constant velocity, and constant acceleration targets to demonstrate the effectiveness of the proposed approach. Results of the set-membership filter-based PPN law are compared with the conventional PPN law with noise and the extended Kalman filter-based PPN law. It is shown that the set-membership filter-based PPN law leads to performances similar to those of the conventional PPN law with noise and the extended Kalman filter-based PPN law. Also, it is shown that the set-membership filter has a better overall estimation performance compared to the extended Kalman filter with tighter bounds on the estimation errors." @default.
• W3120437499 created "2021-01-18" @default.
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• W3120437499 date "2021-01-04" @default.
• W3120437499 modified "2023-09-27" @default.
• W3120437499 title "Set-Membership Filtering-Based Pure Proportional Navigation" @default.
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• W3120437499 doi "https://doi.org/10.2514/6.2021-1567" @default.
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