UAV Formation and Resource Allocation Optimization for Communication-Assisted 3D InSAR Sensing

TL;DR

This paper presents a joint optimization framework for UAV formation and resource allocation to enhance 3D InSAR sensing accuracy and coverage, considering practical metrics and constraints, validated through extensive simulations.

Contribution

It introduces a novel alternating optimization approach for joint UAV formation, speed, and power allocation tailored to InSAR sensing, addressing non-convex challenges.

Findings

UAV-GS communication link significantly affects formation and sensing performance.

Large-area DEM mapping with centimeter-scale accuracy is achievable.

Lower UAV velocities improve InSAR sensing precision.

Abstract

In this paper, we investigate joint unmanned aerial vehicle (UAV) formation and resource allocation optimization for communication-assisted three-dimensional (3D) synthetic aperture radar (SAR) sensing. We consider a system consisting of two UAVs that perform bistatic interferometric SAR (InSAR) sensing for generation of a digital elevation model (DEM) and transmit the radar raw data to a ground station (GS) in real time. To account for practical 3D sensing requirements, we use non-conventional sensing performance metrics, such as the SAR interferometric coherence, i.e., the local cross-correlation between the two co-registered UAV SAR images, the point-to-point InSAR relative height error, and the height of ambiguity, which together characterize the accuracy with which the InSAR system can determine the height of ground targets. Our objective is to jointly optimize the UAV formation, speed, and communication power allocation for maximization of the InSAR coverage while satisfying energy, communication, and InSAR-specific sensing constraints. To solve the formulated non-smooth and non-convex optimization problem, we divide it into three sub-problems and propose a novel alternating optimization (AO) framework that is based on classical, monotonic, and stochastic optimization techniques. The effectiveness of the proposed algorithm is validated through extensive simulations and compared to several benchmark schemes. Furthermore, our simulation results highlight the impact of the UAV-GS communication link on the flying formation and sensing performance and show that the DEM of a large area of interest can be mapped and offloaded to ground successfully, while the ground topography can be estimated with centimeter-scale precision. Lastly, we demonstrate that a low UAV velocity is preferable for InSAR applications as it leads to better sensing accuracy.