Cooperative Multistatic Target Detection in Cell-Free Communication Networks

TL;DR

This work considers the target detection problem in a multistatic integrated sensing and communication scenario characterized by the cell-free MIMO communication network deployment and proposes a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected.

Abstract

In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in the network and the delay resolution from the communication signals, i.e., orthogonal frequency division multiplexing (OFDM) signals, we formulate a cooperative sensing problem with coherent data fusion of multiple RUs' observations and propose a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected. Intensive numerical results indicate promising target detection performance of the proposed SBL-based method. Additionally, a theoretical analysis of the considered cooperative multistatic sensing task is provided using the pairwise error probability (PEP) analysis, which can be used to provide design insights, e.g., illumination and beam patterns, for the considered problem.

Figures (3)

• Figure 1: $K$ RUs in a cell-free network alternatively serve as illuminators and receivers to cooperatively sense $L$ point targets in a global 2D coordinate.
• Figure 2: Results under various SNR: (a) compares SBL, OMP, and PEP; (b) compares CFAR; (c) compares on- and off-grid.
• Figure 3: Detection examples under $20$ dBm SNR with on-grid targets in (a); and off-grid targets in (b) and (c).