Bayesian Transfer Learning with Particle Filter for Object Tracking Under Asymmetric Noise Intensities

Using Bayesian transfer learning, we develop a particle filter approach for tracking a nonlinear dynamical motion model in a dual-sensor system where intensities of measurement noise for both sensors are asymmetric. The densities for Bayesian transfer learning are approximated with the sum of weighted particles to improve the tracking performance of the primary sensor, which experiences higher noise intensity compared to the source sensor. Simulation results are presented that validate the effectiveness of the proposed approach compared to an isolated particle filter and transfer learning applied to the unscented Kalman filter and the cubature Kalman filter.