Bayesian optimization approach for tracking the location and orientation of a moving target using far-field data

TL;DR

This paper presents a Bayesian optimization method for accurately tracking the position and orientation of a moving target using far-field scattering data, incorporating shape identification with neural networks.

Contribution

It introduces a novel approach combining derived formulas, Bayesian optimization, and neural networks for efficient inverse scattering-based tracking.

Findings

Effective tracking of moving targets demonstrated in simulations.

Reduced computational cost through Bayesian optimization.

Successful shape identification using neural networks.

Abstract

We investigate the inverse scattering problem for tracking the location and orientation of a moving scatterer using a single incident field. We solve the problem by adopting the optimization approach with the objective function defined by the discrepancy in far-field data. We rigorously derive formulas for the far-field data under translation and rotation of the target and prove that the objective function is locally Lipschitz with respect to the orientation angle at the true angle. By integrating these formulas with the Bayesian optimization approach, we reduce the cost of objective function evaluations. For the instance of an unknown target, machine learning via fully connected neural networks is applied to identify the shape of the target. Numerical simulations for randomly generated shapes and trajectories demonstrate the effectiveness of the proposed method.