Cram\'{e}r-Rao Bound Optimization for Near-Field ISAC with Extended Targets

TL;DR

This paper develops a geometry-aware transmit design for near-field ISAC targeting extended objects, optimizing the Cramér-Rao Bound for better parameter estimation with reduced computational complexity.

Contribution

It introduces a novel CRB-based optimization framework tailored for extended targets in near-field ISAC, incorporating geometry-aware modeling and reduced-dimensional SDR techniques.

Findings

Lower CRB values compared to point-target and geometry-agnostic methods.

Significantly reduced runtime for large array configurations.

Enhanced target parameter estimation accuracy in near-field conditions.

Abstract

Near-field integrated sensing and communication (ISAC) requires target models beyond the point-target abstraction when the target has a non-negligible spatial extent. In this letter, a geometry-aware transmit design is developed for a parametric extended target (ET) described by its center, orientation, and size under spherical-wave propagation. The CRB for the geometric parameters is formulated around a nominal ET state, an exact ET-aware reduced subspace is identified for the lifted covariance formulation, and a reduced-dimensional semidefinite relaxation (SDR) is developed under signal-to-interference-plus-noise ratio (SINR) and power constraints. Simulation results show lower CRB values than point-target and geometry-agnostic baselines together with substantially reduced runtime for large arrays.