Extended Target Sensing in MIMO-OFDM ISAC Systems: Modeling, Optimization and Estimation

TL;DR

This paper introduces a new parametric scattering model for extended target sensing in wideband MIMO-OFDM ISAC systems, enabling improved localization accuracy, reduced computational complexity, and effective handling of range ambiguities.

Contribution

The paper proposes a novel parametric scattering model that decouples target geometry from scattering, along with a range ambiguity mitigation and optimized beamforming framework for enhanced sensing performance.

Findings

Improved target localization accuracy with the proposed model.

Reduced runtime for beamforming optimization and estimation.

Effective range ambiguity suppression while satisfying communication requirements.

Abstract

This paper develops a comprehensive target modeling, beamforming optimization, and parameter estimation framework for extended-target sensing in wideband MIMO-OFDM integrated sensing and communication systems. We propose a parametric scattering model (PSM) that decouples target geometry from electromagnetic scattering characteristics, requiring only six nonlinear geometric parameters and linear radar cross-section (RCS) coefficients. Based on this compact structure, we derive a hybrid Bayesian Cram\'{e}r-Rao bound (CRB) for joint estimation of azimuth, elevation, and range-related parameters. To handle inherent range ambiguities due to OFDM signaling, we analyze the range ambiguity function and introduce range sidelobe suppression constraints around the true range. Based on these constraints, we formulate an ambiguity-aware transmit beamforming design that minimizes a weighted geometric CRB subject to per-user signal-to-interference-plus-noise ratio (SINR) requirements and a total power budget. As benchmarks, we extend two other common models to the same wideband MIMO-OFDM scenario. We also derive maximum a posteriori estimators and a computational complexity analysis for all three models. Simulation results demonstrate that the proposed PSM-based approach achieves improved target localization with significantly reduced runtime for beamforming optimization and parameter estimation, while consistently satisfying communication SINR requirements.