Joint Mobile User Positioning and Passive Target Sensing using Optimized Sequential Beamforming

2026-05-15

AI Summary

This work addresses the limitations of existing integrated sensing and communication frameworks, which typically neglect user mobility and treat monostatic sensing and bistatic localization as independently optimized tasks, thereby hindering efficient collaboration. To overcome this, the paper proposes a velocity-aware sequential beamforming framework that dynamically couples sensing and localization over time through a shared beamformer. A sequential Bayesian optimization strategy is introduced, leveraging monostatic sensing to construct structural priors that are propagated to the localization stage, enabling joint optimization rather than a weighted trade-off. Further performance gains are achieved through Cramér–Rao bound-based position-domain modeling and non-convex resource allocation. The proposed method significantly reduces computational overhead while attaining centimeter-level accuracy for both user and passive target localization, along with robust velocity estimation.

Abstract

Integrated sensing and communication (ISAC) relies on monostatic sensing (MS) and bistatic positioning (BP) to enable comprehensive environmental awareness and user localization. However, existing frameworks predominantly assume static geometries and optimize these modalities independently, neglecting user mobility and sequential information sharing. In this paper, we propose a velocity-aware sequential beamforming framework that dynamically couples MS and BP in time. We derive the Cramer-Rao bounds (CRBs) in the position domain to formulate a non-convex resource allocation problem. Instead of relying on static weighted-sum tradeoffs, we introduce a sequential Bayesian optimization strategy where MS is executed first to construct a reliable structural prior on the UE and passive targets (PTs). This covariance prior is subsequently passed to the UE to regularize the BP estimation stage. We demonstrate that optimizing a single shared beamformer globally across both phases yields superior synergistic gains compared to a two-stage greedy approach. Simulation results validate that the shared sequential design efficiently balances limited symbol resources, achieving centimeter-level positioning accuracy for both the UE and PTs, robust velocity estimation, and a significantly reduced computational runtime.

Problem

• Integrated Sensing and Communication
• Mobile User Positioning
• Passive Target Sensing
• Sequential Beamforming
• User Mobility

Innovation

• sequential beamforming
• integrated sensing and communication (ISAC)
• Cramer-Rao bound
• Bayesian optimization
• velocity-aware positioning