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arXiv 2503.03220eess.SP
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联合双基地定位与单基地感知：优化波束成形与性能折中

Joint Bistatic Positioning and Monostatic Sensing: Optimized Beamforming and Performance Tradeoff

Yuchen Zhang, Hui Chen, Pinjun Zheng, Boyu Ning, Hong Niu, Henk Wymeersch, Tareq Y. Al-Naffouri

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This work has been submitted to IEEE for possible publication
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AI总结：

本文研究多输入多输出正交频分系统中的联合双基地定位与单基地感知，基于Cramér-Rao Bounds提出波束成形优化策略以实现性能折中，涵盖数字与模拟方案，数值结果验证了协方差矩阵加权和失配最小化及互信息融合的优越性。

摘要：

我们在多输入多输出正交频分系统内研究联合双基地定位（BP）与单基地感知（MS）。基于推导出的Cramér-Rao Bounds（CRBs），我们提出了新颖的波束成形优化策略，能够在BP和MS之间实现灵活的性能折中。该多目标优化问题考虑了两个不同目标：使用户设备能够在考虑未知时钟偏移和朝向的情况下估计自身位置，以及允许基站定位无源目标。我们首先分析数字方案，提出了加权和CRB最小化与（波束成形器和协方差矩阵的）加权和失配最小化方法。这些方法在全维波束成形（FDB）和低复杂度基于码本的功率分配（CPA）下进行了研究。为适应低成本硬件，我们基于协方差矩阵加权和失配范式开发了单位幅度模拟FDB和CPA方案，并使用不同方法求解。数值结果验证了我们设计的有效性，突出了最小化协方差矩阵加权和失配的优越性，以及BP与MS之间互信息融合的优势。

英文摘要：

We investigate joint bistatic positioning (BP) and monostatic sensing (MS) within a multi-input multi-output orthogonal frequency-division system. Based on the derived Cramér-Rao Bounds (CRBs), we propose novel beamforming optimization strategies that enable flexible performance trade-offs between BP and MS. Two distinct objectives are considered in this multi-objective optimization problem, namely, enabling user equipment to estimate its own position while accounting for unknown clock bias and orientation, and allowing the base station to locate passive targets. We first analyze digital schemes, proposing both weighted-sum CRB and weighted-sum mismatch (of beamformers and covariance matrices) minimization approaches. These are examined under full-dimension beamforming (FDB) and low-complexity codebook-based power allocation (CPA). To adapt to low-cost hardwares, we develop unit-amplitude analog FDB and CPA schemes based on the weighted-sum mismatch of the covariance matrices paradigm, solved using distinct methods. Numerical results confirm the effectiveness of our designs, highlighting the superiority of minimizing the weighted-sum mismatch of covariance matrices, and the advantages of mutual information fusion between BP and MS.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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