Genetic Algorithm And Its Applications In Passive Bearing-Only Localization Technologies

With the development of radar countermeasures such as stealth measures, electronic jamming and anti-radiation missiles, the active localization systems such as radar is threatened more and more strongly in applications. Due to the ablity to ascertain radiant target snugly and other characteristics such as strong invisibility, far detection range and excellent anti-jamming ablity, passive localization systems can greatly enhance the whole system's viability and fight efficiency. So the research of passive localization technologies is regarded increasely in most countries.This paper mainly describes the applications of genetic algorithm in passive localization technologies. Based on natural selection and natural stochastic gene mechanism in the evolution processes of natural populations, genetic algorithm has the abilities of adaptive, global-search optimization. It can be applied to solve the complicated nonlinear optimization problems in passive bearing-only localization, which are hard to deal with for tranditional methods. After the detailed analysis of Stansfield estimator, the theory of multi-point average technique is discussed and its uses are also developed. To solve the complicated nonlinear optimization problem in ML estimator, a novel ML method is introduced based on 1-D coding improved real coded genetic algorithm, in which Stansfield estimaor is applied for pre-estimation. With analyzing the influence of observer trajectories on the position-estimating precision, a concept of evolution observer trajectory is presented, which spreads the applications of genetic algorithm in passive localization technologies from data-processing to the guardance in observer's course maneuvers and dynamic programming, etc. Simulations show the advantages of using genetic algorithm in passive bearing-only localization systems. This paper can help to conduct the research and development of passive bearing-only localization technologies.