Sensing Accuracy Optimization for Communication-assisted Dual-baseline UAV-InSAR

TL;DR

This paper presents an optimization framework for enhancing sensing accuracy in UAV-based dual-baseline InSAR systems by jointly optimizing UAV formation and power allocation, leading to significant accuracy improvements.

Contribution

It introduces a joint optimization method for UAV formation and power allocation to minimize height estimation error in dual-baseline UAV-InSAR systems.

Findings

Proposed optimization improves sensing accuracy over classical systems.

Simulation results show significant error reduction.

Joint optimization outperforms benchmark schemes.

Abstract

In this paper, we study the optimization of the sensing accuracy of unmanned aerial vehicle (UAV)-based dual-baseline interferometric synthetic aperture radar (InSAR) systems. A swarm of three UAV-synthetic aperture radar (SAR) systems is deployed to image an area of interest from different angles, enabling the creation of two independent digital elevation models (DEMs). To reduce the InSAR sensing error, i.e., the height estimation error, the two DEMs are fused based on weighted averaging techniques into one final DEM. The heavy computations required for this process are performed on the ground. To this end, the radar data is offloaded in real time via a frequency division multiple access (FDMA) air-to-ground backhaul link. In this work, we focus on improving the sensing accuracy by minimizing the worst-case height estimation error of the final DEM. To this end, the UAV formation and the power allocated for offloading are jointly optimized based on alternating optimization (AO), while meeting practical InSAR sensing and communication constraints. Our simulation results demonstrate that the proposed solution can significantly improve the sensing accuracy compared to classical single-baseline UAV-InSAR systems and other benchmark schemes.