CRB Optimization using a Parametric Scattering Model for Extended Targets in ISAC Systems

TL;DR

This paper introduces a parametric scattering model for extended target sensing in ISAC systems, enabling efficient CRB optimization and beamforming design to improve sensing accuracy while maintaining communication quality.

Contribution

The paper proposes a novel parametric scattering model that simplifies target representation and enhances CRB-based optimization in ISAC systems.

Findings

PSM outperforms traditional models in CRB minimization

Proposed beamforming balances sensing and communication performance

Simulation confirms scalability and practical benefits

Abstract

This paper presents a novel parametric scattering model (PSM) for sensing extended targets in integrated sensing and communication (ISAC) systems. The PSM addresses the limitations of traditional models by efficiently capturing the target's angular characteristics through a compact set of key parameters, including the central angle and angular spread, enabling efficient optimization. Based on the PSM, we first derive the Cramer-Rao Bound (CRB) for parameter estimation and then propose a beamforming design algorithm to minimize the CRB while meeting both communication signal-to-interference-plus-noise ratio (SINR) and power constraints. By integrating the PSM into the beamforming optimization process, the proposed framework achieves superior CRB performance while balancing the tradeoff between sensing accuracy and communication quality. Simulation results demonstrate that the PSM-based approach consistently outperforms traditional unstructured and discrete scattering models, particularly in resource-limited scenarios, highlighting its practical applicability and scalability.