Covariance-based Imaging and Multi-View Fusion for Networked Sensing

TL;DR

This paper introduces a covariance-based multi-view imaging method for 6G networks that jointly processes signals from multiple base stations to produce high-quality images of extended targets.

Contribution

It presents a novel two-phase framework combining covariance-based imaging and advanced fusion techniques to improve multi-view target reconstruction in networked sensing.

Findings

Significant enhancement in imaging quality demonstrated through extensive simulations.

Effective joint estimation of scattering intensity and target geometry.

Robust fusion method that aligns heterogeneous images onto common grids.

Abstract

This paper considers multi-view imaging in a sixth-generation (6G) integrated sensing and communication network, which consists of a transmit base-station (BS), multiple receive BSs connected to a central processing unit (CPU), and multiple extended targets. Our goal is to devise an effective multi-view imaging technique that can jointly leverage the targets' echo signals at all the receive BSs to precisely construct the image of these targets. To achieve this goal, we propose a two-phase approach. In Phase I, each receive BS recovers an individual image based on the sample covariance matrix of its received signals. Specifically, we propose a novel covariance-based imaging framework to jointly estimate effective scattering intensity and grid positions, which reduces the number of estimated parameters leveraging channel statistical properties and allows grid adjustment to conform to target geometry. In Phase II, the CPU fuses the individual images of all the receivers to construct a high-quality image of all the targets. Specifically, we design edge-preserving natural neighbor interpolation (EP-NNI) to map individual heterogeneous images onto common and finer grids, and then propose a joint optimization framework to estimate fused scattering intensity and BS fields of view. Extensive numerical results show that the proposed scheme significantly enhances imaging performance, facilitating high-quality environment reconstruction for future 6G networks.