Evaluation of Guidance Laws Performance Sing Genetic Algorithm
Several conditions affect the performance of guidance law like target parameters or delayed line of sight rate. A variable navigation ratio is used to enhance the performance of guidance law. In this paper a Genetic Algorithm is used to formulate different forms of variable gains and measure the miss distance. An optimization process is running to find the minimum miss distance. The average values and standard deviation of miss distance for all genetic algorithm individuals are calculated to measure the performance and robustness of guidance law. Two guidance laws are considered proportional navigation (PN) and differential geometry (DG). The simulation results show that the proportional navigation is superior to differential geometry performance in the presence of delayed line of sight rate.