UAV-Borne Digital Radar System for Coherent Multistatic SAR Imaging

Advancements in analog-to-digital converter (ADC) technology have enabled higher sampling rates, making it feasible to adopt digital radar architectures that directly sample the radio-frequency (RF) signal, eliminating the need for analog downconversion. This digital approach supports greater flexibility in waveform design and signal processing, particularly through digital modulation schemes like orthogonal frequency division multiplexing (OFDM). This paper presents a digital radar system mounted on an uncrewed aerial vehicle (UAV), which employs OFDM waveforms for coherent multistatic synthetic aperture radar (SAR) imaging in the L-band. The radar setup features a primary UAV node responsible for signal transmission and monostatic data acquisition, alongside secondary nodes that operate in a receive-only mode. These secondary nodes capture the radar signal reflected from the scene as well as a direct sidelink signal. RF signals from both the radar and sidelink paths are sampled and processed offline. To manage data storage efficiently, a trigger mechanism is employed to record only the relevant portions of the radar signal. The system maintains coherency in both fast-time and slow-time domains, which is essential for multistatic SAR imaging. Because the secondary nodes are passive, the system can be easily scaled to accommodate a larger swarm of UAVs. The paper details the full signal processing workflow for both monostatic and multistatic SAR image formation, including an analysis and correction of synchronization errors that arise from the uncoupled operation of the nodes. The proposed coherent processing method is validated through static radar measurements, demonstrating coherency achieved by the concept. Additionally, a UAV-based bistatic SAR experiment demonstrates the system's performance by producing high-resolution monostatic, bistatic, and combined multistatic SAR images.

Robust Trajectory and Resource Optimization for Communication-assisted UAV SAR Sensing

In this paper, we investigate joint 3-dimensional (3D) trajectory planning and resource allocation for rotary-wing unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) sensing. To support emerging real-time SAR applications and enable live mission control, we incorporate real-time communication with a ground station (GS). The UAV's main mission is the mapping of large areas of interest (AoIs) using an onboard SAR system and transferring the unprocessed raw radar data to the ground in real time. We propose a robust trajectory and resource allocation design that takes into account random UAV trajectory deviations. To this end, we model the UAV trajectory deviations and study their effect on the radar coverage. Then, we formulate a robust non-convex mixed-integer non-linear program (MINLP) such that the UAV 3D trajectory and resources are jointly optimized for maximization of the radar ground coverage. A low-complexity sub-optimal solution for the formulated problem is presented. Furthermore, to assess the performance of the sub-optimal algorithm, we derive an upper bound on the optimal solution based on monotonic optimization theory. Simulation results show that the proposed sub-optimal algorithm achieves close-to-optimal performance and not only outperforms several benchmark schemes but is also robust with respect to UAV trajectory deviations.