Practical Hardware-Efficient ISAC: Enabling Multi-Target Sensing With Few RF Chains

Abstract

Hybrid analog--digital arrays reduce hardware cost in integrated sensing and communications (ISAC), but spatial compression and beam-dependent scan responses complicate multi-target estimation with few radio-frequency (RF) chains. To address these challenges, this paper proposes a burst beam-scanning multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) framework that reuses downlink reference signals for joint range, radial-velocity, elevation, and azimuth estimation. Specifically, a structured global--local dual-tensor estimator separates targets by using scanning observations and refines their parameters from selected-beam RF-chain responses, with delay--Doppler signatures maintaining cross-stage target correspondence. The recovered subarray responses yield closed-form elevation and azimuth estimates through exact sum--difference relations, avoiding small-angle approximations and fine angular grid searches. To further improve Doppler accuracy, spatial-mask-aided weighted least squares exploits the longer inter-burst temporal aperture while resolving phase ambiguity using intra-burst estimates. The joint multi-target Cram\'{e}r--Rao lower bound (CRLB) and corresponding three-dimensional (3D) positioning bound are also derived to characterize estimation performance. Simulation results show that the proposed scheme achieves near-CRLB estimation accuracy at high transmit powers and centimeter- to millimeter-level 3D positioning with only four RF chains, while maintaining low computational cost.