Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network

2026-03-11

AI Summary

This work addresses the challenges of high latency, excessive energy consumption, and privacy leakage in cross-view localization within sixth-generation (6G) space-air-ground integrated networks (SAGIN). To tackle these issues, the authors propose a hierarchical inference framework that, for the first time, deeply integrates cross-view localization with SAGIN architecture, establishing a joint optimization paradigm encompassing communication, computation, and privacy. By leveraging distributed resources across the network, the proposed approach significantly enhances localization accuracy, processing speed, and energy efficiency while ensuring strong privacy protection. Experimental results validate the effectiveness of the framework, offering a novel and practical pathway toward intelligent localization systems in 6G environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Large-scale security measurements

Abstract

Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster processing speed. Then, we propose a split-inference framework for implementing CVL, which fully leverages the distributed communication and computing resources of the 6G SAGIN architecture. Subsequently, we conduct joint optimization of communication, computation, and confidentiality within the proposed split-inference framework, aiming to provide a paradigm and a direction for making CVL efficient. Experimental results validate the effectiveness of the proposed framework and provide solutions to the optimization problem. Finally, we discuss potential research directions for 6G SAGIN-enabled CVL.

Problem

• Cross-View Localization
• 6G
• Space-Air-Ground Integrated Network
• Efficient Localization
• Privacy Protection

Innovation

• Cross-View Localization
• 6G SAGIN
• Split-Inference
• Joint Optimization
• Privacy-Preserving Localization