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

TL;DR

This paper introduces a velocity-aware sequential beamforming framework for integrated sensing and communication that enhances user and target localization by coupling monostatic sensing and bistatic positioning in a dynamic, resource-efficient manner.

Contribution

It proposes a novel sequential Bayesian optimization approach that couples sensing and positioning, optimizing a shared beamformer for improved accuracy and efficiency.

Findings

Achieves centimeter-level positioning accuracy for user equipment and passive targets.

Demonstrates robust velocity estimation and reduced computational runtime.

Validates superior performance of shared sequential design over two-stage approaches.

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.