发表机构
The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出渐进式多目标MIMO感知框架，通过变分贝叶斯推断以多项式复杂度近似后验PDF，最小化PCRB并逐步提升感知精度。
AI 中文摘要
本文提出了一种渐进式贝叶斯多输入多输出（MIMO）感知框架，用于在多个阶段对多个目标进行感知。我们考虑了一个实际且具有挑战性的场景，其中目标角度是未知的随机参数，需要被估计，而目标的反射系数是未知的干扰参数。利用目标角度的初始先验概率密度函数（PDF），我们将每个感知阶段的先验PDF逐步更新为前一阶段获得的后验PDF，并在此基础上进行贝叶斯发射波束成形优化，以最小化估计目标角度时的总和后验克拉美-罗下界（PCRB），同时在该阶段借助新的观测数据进行贝叶斯感知。为了以低复杂度解析地表征难以处理且高维的后验PDF，我们提出了一种基于变分贝叶斯推断的方法，该方法以闭式形式推导出一个替代后验PDF，其复杂度仅随目标数量呈多项式增长，这与现有指数复杂度的数值计算方法形成鲜明对比。数值结果验证了我们提出的框架在逐步细化感知性能方面的有效性。
英文摘要
This paper proposes a progressive Bayesian multiple-input multiple-output (MIMO) sensing framework for multiple targets over multiple stages. We consider a practical yet challenging scenario where the targets' angles are unknown and random parameters to be estimated, while the targets' reflection coefficients are unknown nuisance parameters. With an initial prior probability density function (PDF) for the targets' angles, we progressively update the prior PDF for each sensing stage as the posterior PDF obtained from the previous stage, based on which Bayesian transmit beamforming optimization is performed to minimize the sum posterior Cramér-Rao bound (PCRB) in estimating the targets' angles and Bayesian sensing is performed with the help of new observations in this stage. To analytically characterize the intractable and high-dimensional posterior PDF with low complexity, we propose a variational Bayesian inference based approach which derives a surrogate posterior PDF in closed form with only polynomial complexity over the number of targets, in sharp contrast to existing numerical calculation approaches with exponential complexity. Numerical results validate the efficacy of our proposed framework in progressively refining sensing performance.
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