Multi-static Parameter Estimation in the Near/Far Field Beam Space for Integrated Sensing and Communication Applications

TL;DR

This paper introduces a two-stage maximum likelihood parameter estimation framework for mmWave ISAC systems that adaptively switches between near-field and far-field assumptions, improving accuracy and communication performance.

Contribution

It presents a novel hybrid estimation approach that accounts for near-field effects in large array systems, enhancing parameter accuracy in integrated sensing and communication.

Findings

Near-field estimation improves target parameter accuracy.

Beamfocusing codewords increase communication performance.

Two-stage approach adapts to target location for better results.

Abstract

This work proposes a maximum likelihood (ML)-based parameter estimation framework for a millimeter wave (mmWave) integrated sensing and communication (ISAC) system in a multi-static configuration using energy-efficient hybrid digital-analog arrays. Due to the typically large arrays deployed in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field regime. The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field assumptions, and is used to obtain a first estimate of the target parameters. In cases where the target is determined to be in the near-field of the arrays, a second estimation based on near-field assumptions is carried out to obtain more accurate estimates. In particular, we select beamfocusing array weights designed to achieve a constant gain over an extended spatial region and re-estimate the target parameters at the receivers. We evaluate the effectiveness of the proposed framework in numerous scenarios through numerical simulations and demonstrate the impact of the custom-designed flat-gain beamfocusing codewords in increasing the communication performance of the system.