Cooperative Multi-Satellite ISAC Networks: Centralized vs. Distributed Sensing

TL;DR

This paper explores cooperative sensing in multi-satellite ISAC networks, proposing centralized and distributed frameworks that improve target localization accuracy while balancing communication and sensing performance.

Contribution

It introduces novel centralized and distributed sensing frameworks with optimized beamforming and data association for multi-satellite ISAC systems.

Findings

Both frameworks outperform existing sensing schemes.

Distributed framework reduces signaling overhead.

Trade-offs between sensing accuracy and power consumption are analyzed.

Abstract

This paper investigates a downlink multi-satellite integrated sensing and communication (ISAC) network, in which multiple satellites simultaneously transmit ISAC signals to provide communication services to ground user equipments and enable cooperative sensing of airborne targets through multiple gateways. To support this dual functionality, we introduce communication and sensing beamforming designs based on uniform planar arrays with optimized power allocation. Building on these designs, we propose two cooperative sensing frameworks, namely centralized and distributed. In the centralized framework, each gateway forwards its sensing observations to a central unit (CU), where the positions of multiple targets are jointly estimated from the aggregated data using a sparse signal recovery formulation. To mitigate the signaling overhead inherent in centralized processing, a distributed framework is further proposed, in which each gateway independently estimates target positions and transmits only the local estimates to the CU. To associate estimates from different gateways, a data association problem based on the squared Euclidean distance is formulated and efficiently solved using the Hungarian algorithm. The final target positions are then obtained by minimizing the distance estimation error. Simulation results demonstrate that the proposed centralized and distributed frameworks significantly outperform existing sensing schemes while satisfying communication performance requirements. We also evaluate the sensing-communication trade-off from the viewpoints of sensing accuracy and communication power consumption under the proposed frameworks.