Cooperative Target Detection in Dual-Base Station-Enabled ISAC Systems

This paper considers an integrated sensing and communication (ISAC) system, where two dual-functional base stations (BSs) serve their users and detect multiple targets. To improve detection accuracy while meeting communication quality of service, this paper proposes a two-phase cooperative target detection algorithm that relies on Capon-based adaptive beamforming and maximum likelihood estimation (MLE)-based hypothesis testing. Specifically, based on Capon’s detection results, the two BSs first scan targets with an omnidirectional beam and then track targets with a directional beam. Subsequently, multiple hypotheses regarding the locations of targets are established based on the detection results of the Capon method, and the MLE is employed for hypothesis testing to filter out ghost targets. Finally, simulation results show that the proposed algorithm achieves more precise angles-of-arrival estimation of multiple targets than conventional single-BS sensing, and enables high-precision localization by eliminating ghost targets.