Multi-Target Localization and Association in Cell-Free Massive Mimo for Multi-Static Isac

This paper investigates the problem of localizing and associating multiple targets in an integrated sensing and communication (ISAC) system that employs a multi-static cell-free massive multiple-input multiple-output ($\mathbf{C F}-\mathbf{m M I M O}$) architecture. In this system, a large area is covered by a number of distributed access points (APs). The problem of simultaneously detecting and locating multiple targets is considered to be crucial and challenging, particularly in order to avoid interference in the communications functionalities. By using the virtual channel representation of both the sensing and communication channels and the angular estimation method, e.g., estimation of signal parameters via rotational invariance techniques (ESPRIT), we can first accurately identify specific angular directions from unidentified targets to each receiving (Rx)-AP. Then, we transform the association problem into a clustering problem. We propose a low-complexity approach based on the clustering algorithm to solve this association problem. The proposed method yields robust communication performance while simultaneously achieving outstanding association and localization performance.