Robust Multitarget Localization With Uncalibrated UAV Arrays: A Two-Stage Self-Calibration Method

The unmanned aerial vehicle (UAV) array equipped with sensors is widely used for target localization owing to its superior maneuverability. Unfortunately, limited by the current manufacturing technology, sensor arrays usually exhibit inconsistent gain and phase responses across channels, i.e., gain-phase errors, which can seriously affect the target localization accuracy. Herein, we consider that the gain-phase consistency of all array channels is not been precalibrated. For accurate target localization, we develop a system architecture for bistatic multiple-input-multiple-output (MIMO) radar equipped with a UAV array at the receiver part to realize angle estimation. First, the UAV array is controlled to move near the transmitter to receive the transmitted signals directly. Therefore, the gain-phase consistency of the transmitter can be calibrated by using the data after matched filtering and combining the known relative position information of the transmitter and receiver. Second, we control UVAs away from the transmitter to form a bistatic MIMO radar and use the synthetic aperture technique introduced by the UAV array motion to convert the receive array into partially calibrated. Meanwhile, the array manifold matrices with unknown model errors can be obtained by parallel factor decomposition. Finally, the angle estimates, gain-phase errors, and position errors are estimated by the element-wise division of the steering vectors without iteration. Moreover, our method is insensitive to sensor position errors of the original UAV array while determining angles. Simulation results demonstrate that the proposed method can obtain accurate angle estimates under the aforementioned model errors.