Joint Trajectory and Resource Optimization for HAPs-SAR Systems with Energy-Aware Constraints

TL;DR

This paper presents a joint optimization framework for trajectory and resource management in HAPs-SAR systems, incorporating energy harvesting and real-time data transmission to improve coverage and sustainability.

Contribution

It introduces a novel energy-aware optimization model and a SCA-based solution for efficient trajectory and resource allocation in HAPs-SAR systems.

Findings

The proposed algorithm converges reliably in simulations.

The framework effectively balances SAR coverage, communication, and energy use.

Practical SAR imaging confirms the system's feasibility.

Abstract

This paper investigates the joint optimization of trajectory planning and resource allocation for a high-altitude platform stations synthetic aperture radar (HAPs-SAR) system. To support real-time sensing and conserve the limited energy budget of the HAPs, the proposed framework assumes that the acquired radar data are transmitted in real time to a ground base station for SAR image reconstruction. A dynamic trajectory model is developed, and the power consumption associated with radar sensing, data transmission, and circular flight is comprehensively analyzed. In addition, solar energy harvesting is considered to enhance system sustainability. An energy-aware mixed-integer nonlinear programming (MINLP) problem is formulated to maximize radar beam coverage while satisfying operational constraints. To solve this challenging problem, a sub-optimal successive convex approximation (SCA)-based framework is proposed, incorporating iterative optimization and finite search. Simulation results validate the convergence of the proposed algorithm and demonstrate its effectiveness in balancing SAR performance, communication reliability, and energy efficiency. A final SAR imaging simulation on a 9-target lattice scenario further confirms the practical feasibility of the proposed solution.