Separation and Tracking of Maneuvering Sources with ICA and Particle Filters using a New Switching Dynamic Model

The problem addressed in this work is to simultaneously separate multiple maneuvering sources and track their kinematics (position, velocity, and acceleration) in the working space. It is developed upon the incorporation of the nonstationary independent component analysis (ICA) and the nonlinear state estimator problems in a noisy environment. The sampling importance resampling (SIR) particle filter is exploited as the nonlinear state estimator to track current kinematics of the sources even though the state densities are non-Gaussian, and the observation equations are nonlinear. Given source kinematics, nonstationary ICA with a generalized Gaussian density function is used to separate each source signal. Also a novel scheme is proposed as a better alternative for the conventional interacting multiple model (IMM) algorithm to cover the unpredictable movement of the source over time. The proposed scheme deals with the uncertainty of each source motion by incorporating multiple dynamic models in the tracking process. The single best dynamic mode is identified at each time step for all the sources rather than tracking sources for several IMMs as in the IMM algorithm by finding the mode that tracks an indicator source with minimum root mean square error (RMSE). The method is strictly causal and can be used for online tracking. The algorithm performance has been verified by illustrating some simulation results.