Near-Field Motion Parameter Estimation: A Variational Bayesian Approach

Abstract

A near-field motion parameter estimation method is proposed. In contrast to far-field sensing systems, the near-field sensing system leverages spherical-wave characteristics to enable full-vector location and velocity estimation. Despite promising advantages, the near-field sensing system faces a significant challenge, where location and velocity parameters are intricately coupled within the signal. To address this challenge, a novel subarray-based variational message passing (VMP) method is proposed for near-field joint location and velocity estimation. First, a factor graph representation is introduced, employing subarray-level directional and Doppler parameters as intermediate variables to decouple the complex location-velocity dependencies. Based on this, the variational Bayesian inference is employed to obtain closed-form posterior distributions of subarray-level parameters. Subsequently, the message passing technique is employed, enabling tractable computation of location and velocity marginal distributions. Two implementation strategies are proposed: 1) System-level fusion that aggregates all subarray posteriors for centralized estimation, or 2) Subarray-level fusion where locally processed estimates from subarrays are fused through Guassian product rule. Cramér-Rao bounds for location and velocity estimation are derived, providing theoretical performance limits. Numerical results demonstrate that the proposed VMP method outperforms existing approaches while achieving a magnitude lower complexity. Specifically, the proposed VMP method achieves centimeter-level location accuracy and sub-m/s velocity accuracy. It also demonstrates robust performance for high-mobility targets, making the proposed VMP method suitable for real-time near-field sensing and communication applications.