Study On Passive Localization Technology On Single Obserer

Passive localization and tracking system plays an important role in the electronic reconnaissance, as it works silently without electromagnetic radiation and covering larger region.Single observer passive localzaition and tracking system avoid time synchronization and data fusion,so it attracts more and more research focus.Essentially, passive localization and tracking technology consists of localization and tracking methods and algorithms. They are the two key points of passive localization technology, which decide the precision and rapidity. Thus, the dissertation does deeper researches on the two aspects.The dissertation introduces the model of two-dimensional single-observer passive location using bearing and its changing rate information .Specially, the dissertation does deeper researches on some filtering estimation algorithm.Firstly, background of single observer passive localization is introduce. As the observability and model development are the fundamental problem, they are discussed in dissertation.Followly, some considerable estimation algorithms of localization and tracking are presented. EKF algorithms is the most classical nonlinear method, successfully applying in many passive localization problems. The computer simulations are carried out and the performance in practical application is analyzed. Followly, in order to avoid the weakness of EKF, UKF algorithm in passive localization is studied deeply. UKF use a set of assured particles to estimate the posteriors probability, have a good property in the passive localization and tracking problems. whereafter,particle filtering is studied and applied in passive localization system. It use a set of random samples (also called particles) to estimate the posteriors probability, have a good property in the passive localization and tracking problems.Particle filtering algorithm in order to resolve degeneracy of particle frequently carry out resampling.But resampling of classical particle filtering algorithm lead sample impoverishment of particle. Therefore describe genetic particle filter algorithm (GPFA) in dissertation, indicate advanced resampling method, resolve problem of sample impoverishment...