Subspace Fusion Sensing for Cooperative ISAC

TL;DR

This paper introduces a novel subspace fusion sensing algorithm for cooperative integrated sensing and communication, leveraging tensor methods and EVA arrays to improve sensing accuracy in distributed AP systems.

Contribution

It develops a data association-free subspace-based fusion sensing method using tensor unfolding and EVA arrays, with theoretical CRLB analysis and validation through simulations.

Findings

The proposed algorithm outperforms traditional sensing techniques.

CRLB analysis confirms the theoretical efficiency of the method.

Simulation results demonstrate improved sensing accuracy.

Abstract

This paper proposes a subspace fusion sensing algorithm for cooperative integrated sensing and communication. First, we stack the received signals from access points (APs) into a third-order tensor and construct the equivalent virtual antenna (EVA) array via tensor unfolding. Then, a data association-free subspace-based fusion sensing algorithm is developed utilizing the EVA arrays from distributed APs. A derivation of Cramer-Rao lower bound (CRLB) is also presented. Finally, simulation results validate the effectiveness of the proposed algorithm compared to traditional techniques.