Joint Target Assignment and Resource Allocation for Multi-Base Station Cooperative ISAC in UAV Detection

Integrated sensing and communication (ISAC) is a foundational technology for sixth-generation (6G) networks, with networked ISAC enabling multiple distributed sensing nodes to collaborate in target detection. This paper addresses the critical challenge of managing dynamic interference in cooperative ISAC systems, focusing on fast-moving unmanned aerial vehicles (UAVs) operating in complex propagation environments. Rapid UAV motion induces significant Doppler shifts and fluctuating interference, leading to uneven signal quality and degraded detection performance, particularly in high-interference zones. To tackle this, we propose a fairness-based resource allocation framework that prioritizes the weakest links by maximizing the minimum radar sensing signal-to-interference-plus-noise ratio (SINR). We formulate a joint optimization problem for ISAC beamforming and target allocation, ensuring communication quality of service (QoS) and base station (BS) power constraints. Although the problem is non-convex, we develop an alternating optimization approach. Optimal receive beamforming is derived using the generalized Rayleigh quotient, while transmit beamforming and target assignment are solved via Dinkelbach's method, exploiting strong duality. Numerical results demonstrate the proposed algorithm's superiority over benchmarks, achieving robust and equitable sensing performance across UAVs, even in dynamic, high-mobility scenarios. These results highlight the potential of cooperative ISAC with multiple BSs for reliable target detection and communication under challenging conditions.