The Research On Joint Registration, Association, And Parameter Estimation For Passive Tracking System

On account of advantages like wide radar coverage and goodsheltered performance, passive tracking is playing an irreplaceable role inmodernized war. However, suboptimal performance or divergence of theresult may occur when using a nonlinear filter in a bias tracking systemwith unknown initial state and covariance of noise. Little literature isdealing with this problem. It is thus meaningful to come up with a jointregistration, association, and parameter estimation algorithm in field ofpassive tracking and design a data fusion and parameter estimationsimulation system with high expansibility and applicability.After referring to many advanced registration and associationalgorithms especially Expectation-Maximization algorithm which has arelatively better performance in the field of parameter estimation formixture models with latent variables, I proposed a novel joint registration,association, and parameter estimation algorithm for passive biasedtracking system. The ML estimation of complete data log is done byEM-UKS iteratively till parameter convergence. The association andregistration parameters are estimated in M-step, while the target states areupdated in the E-step by UKS. Simulation shows better estimationperformance than augmented UKF, and similar association performancewith three data fusion algorithm. Based on the analysis mentioned above, a multi-sensor multi-targetsimulation software system is built and described at the end of this thesis.Applicability and expansibility are guaranteed by its friendly UI design,server-client network structure, and open algorithm interface.