Integrated Target Assignment and Trajectory Optimization for Many-to-Many Midcourse Guidance

This article investigates the problem of target assignment and trajectory optimization in multimissile multitarget engagement scenarios. To avoid complex mixed-integer nonlinear programming problems, most of the existing methods solve the assignment and trajectory optimization separately, which cann...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 61; no. 1; pp. 853 - 867
Main Authors: Jin, Tianyu, Shin, Hyo-Sang, Tsourdos, Antonios, He, Shaoming
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Decoupling
Differential dynamic programming (DDP)
Dynamic programming
Heuristic algorithms
Iterative methods
Linear programming
Midcourse guidance
Missiles
Mixed integer
mixed-integer nonlinear programming (MINLP)
Nonlinear programming
Optimal control
Optimization
Programming
Quadratic programming
Resource management
target assignment (TA)
Terminal constraints
Trajectory optimization
trajectory optimization (TO)
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:This article investigates the problem of target assignment and trajectory optimization in multimissile multitarget engagement scenarios. To avoid complex mixed-integer nonlinear programming problems, most of the existing methods solve the assignment and trajectory optimization separately, which cannot fully exploit the synthetic effect between these two functional blocks. To address this dilemma, we propose a new approach based on differential dynamic programming, which can solve assignment and trajectory optimization integrally instead of decoupling them. The proposed method embeds the target assignment problem into terminal constraints and adds the allocation matrix as parameters to the computational process of differential dynamic programming. In order to find the local optimal allocation scheme, a mixed-integer quadratic programming problem needs to be solved in each iteration. The advantages of the proposed method are that the allocation and trajectory optimization share the same objective function and are solved uniformly with an acceptable time consumption. Extensive numerical simulations and comparisons demonstrate the effectiveness of the proposed method.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3448408