用神经网络学习无线定位的完整后验分布，提升精度与不确定性量化。
Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization
- 基于蒙特卡洛采样训练神经评分网络，比较真实与候选位置
- 在视距场景下实现更低交叉熵损失，优于均匀与高斯基线
- 可捕捉角度模糊性与天线前后不对称等关键特性，适合高可靠性系统
现代无线系统不仅需要位置估计，还需量化不确定性以支持规划、控制和无线资源管理。本文将定位问题建模为从接收机测量中推断未知发射机位置的后验推断。提出蒙特卡洛候选似然估计（MC-CLE），通过蒙特卡洛采样训练神经评分网络，比较真实与候选发射机位置。在多天线接收机的视距仿真中，MC-CLE 学习到了角度模糊性和天线前后方向图等关键特性，且在交叉熵损失上显著低于均匀基线和高斯后验。在统一损失度量下表现更优。
原文摘要 · Abstract (English)
Modern wireless systems require not only position estimates, but also quantified uncertainty to support planning, control, and radio resource management. We formulate localization as posterior inference of an unknown transmitter location from receiver measurements. We propose Monte Carlo Candidate-Likelihood Estimation (MC-CLE), which trains a neural scoring network using Monte Carlo sampling to compare true and candidate transmitter locations. We show that in line-of-sight simulations with a multi-antenna receiver, MC-CLE learns critical properties including angular ambiguity and front-to-back antenna patterns. MC-CLE also achieves lower cross-entropy loss relative to a uniform baseline and Gaussian posteriors. alternatives under a uniform-loss metric.
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