用于分数延迟-多普勒OTFS-ISAC的离网变分贝叶斯参数估计
Off-grid Variational Bayesian Parameter Estimation for Fractional Delay-Doppler OTFS-ISAC
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中文总结 AI 辅助
研究基于OTFS的ISAC系统中分数延迟-多普勒估计问题，提出离网变分贝叶斯方法，利用可分离导向向量和冯·米塞斯分布建模，通过闭式变分更新实现自动路径数估计，相比传统方法有更高估计精度。
中文摘要 AI 辅助
本文提出一种用于基于OTFS的集成感知与通信（ISAC）系统中分数延迟-多普勒（DD）估计的离网变分贝叶斯（OVB）方法。通过使用可分离的延迟和多普勒导向向量重新构建OTFS信道，并对相应相位变量采用冯·米塞斯分布建模来实现离网参数估计。闭式变分更新提供后验统计以识别显著路径并修剪冗余候选，实现自动路径数估计。仿真结果表明该方法比传统分数DD估计方法具有更高的信道和参数估计精度。
英文摘要
This letter proposes an off-grid variational Bayesian (OVB) method for fractional delay-Doppler (DD) estimation in OTFS-based integrated sensing and communication (ISAC) systems. To enable off-grid parameter estimation, the OTFS channel is reformulated using separable delay and Doppler steering vectors, and the corresponding phase variables are modeled by von Mises distributions. Closed-form variational updates provide posterior statistics for identifying significant paths and pruning redundant candidates, enabling automatic path-number estimation. Simulation results demonstrate that the proposed method achieves higher channel and parameter estimation accuracy than conventional fractional DD estimation approaches.
