  Beam alignment is essential to compensate for the high path loss in the
millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated
sensing and communication (ISAC) technology has been envisioned as a promising
solution to enable efficient beam alignment in the dynamic UAV network.
However, since the digital identity (D-ID) is not contained in the reflected
echoes, the conventional ISAC solution has to either periodically feed back the
D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by
the separation of D-ID and physical identity (P-ID). This paper presents a
novel dual identity association (DIA)-based ISAC approach, the first solution
that enables specific, fast, and accurate beamforming towards multiple UAVs. In
particular, the P-IDs extracted from echo signals are distinguished dynamically
by calculating the feature similarity according to their prevalence, and thus
the DIA is accurately achieved. We also present the extended Kalman filtering
scheme to track and predict P-IDs, and the specific beam is thereby effectively
aligned toward the intended UAVs in dynamic networks. Numerical results show
that the proposed DIA-based ISAC solution significantly outperforms the
conventional methods in association accuracy and communication performance.
