This paper proposes a cooperative localization method based on time-division processing of interferometric measurements, in which the receiver updates the signals from multiple UAVs in separate time slots, thereby reducing spectrum usage and baseband hardware overhead. A Kalman-enhanced tracking loop is designed to achieve high-precision carrier-phase and Doppler estimation under low-SNR conditions. For angle estimation, a time-division update strategy is employed such that the receiver performs full carrier tracking for only one UAV in each time slot, while the carrier phases of the remaining UAVs are extrapolated from the Doppler states estimated in the previous epoch. This avoids the hardware complexity associated with maintaining multiple parallel tracking loops. By fusing the estimated azimuth, elevation, and pseudorange measurements with the master UAV’s high-precision GNSS observations, a factor-graph-based sliding-window cooperative localization algorithm is constructed. Simulation results show that the proposed method improves the RMSE of carrier-phase and Doppler estimation by nearly an order of magnitude compared with the traditional FLL-assisted PLL. The system maintains angle estimation accuracy better than 0.01° within a four-node configuration and achieves centimeter-level ranging accuracy when SNR ≥ 0 dB. In a cooperative flight scenario with one master and three follower UAVs, the method consistently delivers sub-decimeter 3D localization accuracy.
Li et al. (Wed,) studied this question.