Incorporating Bayesian Transfer Learning into Particle Filter for Dual-Tracking System with Asymmetric Noise Intensities

TL;DR

This paper introduces a Bayesian transfer learning-enhanced particle filter for dual-tracking systems with asymmetric sensor noise, improving tracking accuracy especially when noise disparities are large, validated through simulations.

Contribution

It develops a novel particle filter approach incorporating Bayesian transfer learning to handle asymmetric noise in dual sensors, outperforming traditional filters.

Findings

Transfer learning improves particle filter performance with higher noise disparity.

Increasing particles enhances accuracy but raises computational cost.

Performance gain is proportional to noise intensity difference.

Abstract

Using Bayesian transfer learning, we develop a particle filter approach for tracking a nonlinear dynamical model in a dual-tracking 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 a higher noise intensity compared to the source sensor. We present simulation results 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. Furthermore, increasing the number of particles shows an improvement in the performance of transfer learning applied to the particle filter with a higher rate compared to the isolated particle filter. However, increasing the number of particles raises computational time per step. Moreover, the performance gain from incorporating Bayesian transfer learning is approximately linearly proportional to the absolute difference value between the noise intensities of the sensors in the dual-tracking system.