Apply Optimization Algorithm to Develop Parallel Navigation Guidance Law

Abstract

The proposed missile parallel navigation guidance law utilizes the current dynamics of both the target and the missile to compute the line-of-sight rate and applies missile acceleration control command equations within the framework of proportional navigation guidance to control the missile's flight route. In this study, particle swarm optimization is used to continuously optimize the navigation constants and update the missile acceleration control commands, where the line-of-sight rate is defined as the fitness function of particle swarm optimization. The missile moves toward the target in parallel navigation guidance mode when the computed value of the fitness function approaches zero during the optimization process. There are three different pursuit-evasion scenarios in a three-dimensional space in the simulation experiments, where the target information includes noise disturbances to better simulate real target-interceptor engagement conditions. The simulation results prove that the proposed guidance theory is highly effective in intercepting maneuvering targets with high G-force, whereas proportional navigation guidance cannot achieve this effectively. Furthermore, a missile guidance algorithm using particle swarm optimization has been reproduced, which serves as the main motivation for developing the proposed guidance method in this study. The experiments demonstrate that the PSO-based missile guidance algorithm may produce oscillations in the control commands during pursuit-evasion, which could affect both the guidance performance and structural integrity of the missile. However, the proposed guidance method in this study not only mitigates these oscillations but also achieves better overall guidance performance.

DOI: 10.61416/ceai.v27i3.9565