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

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.