发表机构
Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于CRB的任务感知波束成形框架NIM，利用语义地图局部几何最小化边界法向位置误差，显著降低语义定位错误。
AI 中文摘要
许多语义定位任务需要确定一个上下文相关的有意义区域或状态，而非最小化完整笛卡尔位置估计的误差。在此类场景中，聚合定位精度无需与语义决策的精度保持一致。本文开发了一个基于克拉美-罗界（CRB）的框架，利用已知语义地图的局部几何结构来引导上行接收波束成形。以局部边界穿越作为语义误分类的代理指标，我们推导了法向信息最大化（NIM）方法，该方法最小化沿局部语义边界法线方向的位置分量的CRB。在所考虑的单路径视距模型下，最优接收码本可限制在匹配导向矢量及其角度导数所张成的子空间内，从而将设计简化为在匹配空间模式与导数空间模式之间分配测量资源。我们针对一般光滑边界推导了相应的资源分配方案，其中地理围栏和入侵检测分别作为径向和切向的极限情形出现。该框架进一步通过距离归一化的极小极大准则扩展到多区域语义地图，并通过最坏情况和先验加权鲁棒公式扩展到不确定的先验位置。采用有限时隙实现和基于剖面最大似然（ML）估计的观测级蒙特卡洛模拟的数值结果表明，NIM根据任务相关的边界几何结构分配测量资源，并且在所考虑的场景中，相较于经典的平方位置误差界（SPEB）设计，能够显著降低语义误差。实验结果在所研究的运行机制下也紧密遵循基于局部CRB的边界穿越近似。
英文摘要
Many semantic localization tasks require determining a contextually meaningful region or state rather than minimizing the error of a full Cartesian position estimate. In such settings, aggregate localization accuracy need not align with the accuracy of the semantic decision. This paper develops a Cramér--Rao bound (CRB)-based framework that uses the local geometry of a known semantic map to guide uplink receive beamforming. Using local boundary crossing as a surrogate for semantic misclassification, we derive normal information maximization (NIM), which minimizes the CRB of the position component along the local semantic boundary normal. Under the considered single-path line-of-sight model, an optimal receive codebook can be restricted to the subspace spanned by the matched steering vector and its angular derivative, reducing the design to an allocation of measurement resources between matched and derivative spatial modes. We derive the resulting allocation for a general smooth boundary, with geofencing and intrusion detection arising as radial and tangential limiting cases. The framework is further extended to multi-region semantic maps through a distance-normalized minimax criterion and to uncertain prior locations through worst-case and prior-weighted robust formulations. Numerical results with finite-slot implementations and observation-level Monte Carlo simulations using profile maximum-likelihood (ML) estimation show that NIM allocates measurements according to the task-relevant boundary geometry and can substantially reduce semantic error relative to the classic squared position error bound (SPEB) design in the considered scenarios. The empirical results also closely follow the local CRB-based boundary-crossing approximation in the studied operating regime.
Comments18 pages, 8 figures
