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

2025-11-18

AI Summary

In 6G integrated sensing and communication networks, multi-base-station collaborative imaging suffers from inefficient utilization of echo signals and low-fidelity multi-view image fusion. To address these challenges, this paper proposes a covariance-domain-based dual-phase imaging and multi-view fusion method. We innovatively formulate a covariance-domain imaging framework that jointly estimates target scattering intensity and grid-aligned positions. Furthermore, we design an edge-preserving natural neighbor interpolation (EP-NNI) scheme and a joint multi-view optimization fusion model to enable heterogeneous image alignment and cooperative field-of-view modeling. Grid adaptation is achieved by leveraging statistical channel characteristics. Experimental results demonstrate substantial improvements in imaging resolution and fusion accuracy, enabling high-precision environmental perception in complex scenarios. The proposed approach delivers robust, scalable sensing capabilities for 6G networks.

Technology Category

Computer Vision: Multi-modal VisionIntelligent Robots: Multimodal Perception & Sensor FusionMachine Learning: Multi-instance/Multi-view Learning

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSystems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital healthSearch and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Multi-view imaging in 6G networks using distributed base stations
• Fusing heterogeneous echo signals from multiple receivers for precise imaging
• Joint estimation of scattering intensity and grid positions via covariance

Innovation

• Covariance-based imaging with grid position adjustment
• Edge-preserving natural neighbor interpolation for fusion
• Joint optimization for fused scattering intensity estimation