3D Extended Target Sensing in ISAC: Cram\'er-Rao Bound Analysis and Beamforming Design

TL;DR

This paper develops a 3D extended target sensing framework in ISAC systems, deriving CRBs, formulating beamforming optimization problems, and proposing a GNN-based solution to enhance sensing and communication performance.

Contribution

It introduces a 3D ET surface model, derives CRBs for kinematic parameters, and proposes both optimization-based and GNN-based beamforming methods for integrated sensing and communication.

Findings

Proposed beamforming methods outperform existing baselines.

Shape-aware beamforming improves sensing beampatterns.

GNN-based approach offers scalable, adaptable solutions.

Abstract

This paper investigates an integrated sensing and communication (ISAC) system where the sensing target is a three-dimensional (3D) extended target, for which multiple scatterers from the target surface can be resolved. We first introduce a second-order truncated Fourier series surface model for an arbitrarily-shaped 3D ET. Utilizing this model, we derive tractable Cramer-Rao bounds (CRBs) for estimating the ET kinematic parameters, including the center range, azimuth, elevation, and orientation. These CRBs depend explicitly on the transmit covariance matrix and ET shape. Then we formulate two transmit beamforming optimization problems for the base station (BS) to simultaneously support communication with multiple users and sensing of the 3D ET. The first minimizes the sensing CRB while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for each user, and it is solved using semidefinite relaxation. The second balances minimizing the CRB and maximizing communication rates through a weight factor, and is solved via successive convex approximation. To reduce the computational complexity, we further propose ISACBeam-GNN, a novel graph neural network-based beamforming method that employs a separate-then-integrate structure, learning communication and sensing (C&S) objectives independently before integrating them to balance C&S trade-offs. Simulation results show that the proposed beamforming designs that account for ET shapes significantly outperform existing baselines, offering better communication-sensing performance trade-offs as well as an improved beampattern for sensing. Results also demonstrate that ISACBeam-GNN is an efficient alternative to the optimization-based methods, with remarkable adaptability and scalability.