Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters

TL;DR

This paper introduces a transfer learning-enhanced filtering framework that improves object tracking accuracy in nonlinear systems by integrating Bayesian transfer learning into UKF and CKF, outperforming traditional methods.

Contribution

The novel integration of Bayesian transfer learning into UKF and CKF for improved object tracking under noise mismatch conditions is presented.

Findings

Transfer learning significantly improves estimation accuracy.

The proposed method outperforms conventional UKF and CKF.

Comparison shows better results than measurement vector fusion.

Abstract

We present a novel filtering algorithm that employs Bayesian transfer learning to address the challenges posed by mismatched intensity of the noise in a pair of sensors, each of which tracks an object using a nonlinear dynamic system model. In this setting, the primary sensor experiences a higher noise intensity in tracking the object than the source sensor. To improve the estimation accuracy of the primary sensor, we propose a framework that integrates Bayesian transfer learning into an Unscented Kalman Filter (UKF) and a Cubature Kalman Filter (CKF). In this approach, the parameters of the predicted observations in the source sensor are transferred to the primary sensor and used as an additional prior in the filtering process. Our simulation results show that the transfer learning approach significantly outperforms the conventional isolated UKF and CKF. Comparisons to a form of measurement vector fusion are also presented.