ISAC Enabled Cooperative Detection for Cellular-Connected UAV Network

The rapid development of low altitude Unmanned Aerial Vehicles (UAVs) as a new mode of transportation has injected a new driving force into the market development, but at the same time, unreported “black flight” UAVs have also created new risks in civil aviation safety, citizen privacy protection and other social security areas. In this regard, the Integrated Sensing And Communication (ISAC) capability of Base Station (BS) can provide an effective means of communication and supervision of low-altitude UAVs. For example, by demarcating the electronic fence area, the ISAC BS can realize automatic detection of illegal invasion of UAVs, effectively guaranteeing low-altitude safety in the context of low-altitude economy. By leveraging the high mobility of UAVs and their strong air-ground Line-of-Sight (LoS) channels, UAV-enabled ISAC is anticipated to provide superior sensing and communication coverage, and enhanced sensing and communication performance compared to terrestrial ISAC. However, existing work mainly focus on single BS sensing with the assistance of communication, which may not fully activate ISAC’s potential and achieve high-precision long-range sensing. Given the above considerations, this paper provides a cellular-connected UAV system, where the BS and connected UAV are employed to perform cooperative detection tasks for precise detection. To unleash the potential of ISAC in cellular-connected UAV systems, on the one hand, we propose an Extended Kalman Filtering (EKF) based data fusion algorithm to provide precise environment information and achieve beyond LoS sensing. On the other hand, according to the fusion results, we optimize the communication rate performance by jointly designing the transmit beamforming and trajectory subject to the power and practical fight constraints to combat the effect of mobility, while ensuring the sensing requirements, which can achieve a positive feedback loop. Extensive simulation results demonstrate that the proposed data fusion algorithm improves the estimation accuracy by 67% and the joint design of beamforming and trajectory algorithm improves the communication data rate by more than 31%.