Near-Field Motion Parameter Estimation: A Variational Bayesian Approach
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912486883590144 |
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| author | Meng, Chunwei Wang, Zhaolin Wei, Zhiqing Liu, Yuanwei Feng, Zhiyong |
| author_facet | Meng, Chunwei Wang, Zhaolin Wei, Zhiqing Liu, Yuanwei Feng, Zhiyong |
| contents | A near-field motion parameter estimation method is proposed. In contract 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Near-Field Motion Parameter Estimation: A Variational Bayesian Approach Meng, Chunwei Wang, Zhaolin Wei, Zhiqing Liu, Yuanwei Feng, Zhiyong Signal Processing A near-field motion parameter estimation method is proposed. In contract 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. |
| title | Near-Field Motion Parameter Estimation: A Variational Bayesian Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.14193 |