Fusion of Monostatic and Bistatic Sensing for ISAC-Enabled Low-Altitude Environment Mapping

2026-03-09

AI Summary

This work addresses the limitations of existing multipath-based environmental mapping approaches, which are largely confined to bistatic non-line-of-sight (NLOS) links and overlook the inherent monostatic sensing capability of integrated sensing and communication (ISAC) systems as well as diffuse scattering effects caused by non-ideal building facades. To overcome these shortcomings, we propose the first Bayesian multipath mapping framework that jointly exploits both monostatic and bistatic measurements. By introducing a unified geometric model that associates observations from both link types with their common physical reflector, our method enables data-level fusion. The framework incorporates two Bayesian factor graph structures tailored to different scenarios and explicitly models the scattering characteristics of non-ideal surfaces. Experiments on synthetic RF data demonstrate that the proposed approach significantly outperforms single-link baselines, achieving notable improvements in mapping accuracy, robustness, and convergence speed.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Driven by the rapid growth of the low-altitude economy, integrated sensing and communication (ISAC) technologies are essential to meet the stringent demands for reliable connectivity and situational awareness. Within this context, multipath-based simultaneous localization and mapping has emerged as a promising approach by leveraging radio frequency (RF) multipath to reconstruct environment maps alongside agent localization. Nevertheless, existing studies largely confine themselves to bistatic non-line-of-sight links and assume purely specular reflections from smooth surfaces, overlooking the monostatic sensing capability inherent in ISAC systems and the diffuse scattering effects induced by non-ideal outdoor building facades. To address these limitations, this paper presents the first Bayesian multipath-based environment mapping framework for ISAC that integrates monostatic and bistatic measurements under non-ideal surface propagation. We establish geometric relationships linking both sensing modes to a common reflective surface, enabling their association with the same physical feature for data-level fusion. Building on this formulation, we design two complementary Bayesian frameworks with corresponding factor-graph representations, allowing flexible adaptation to different scene requirements. The effectiveness of the proposed approach is validated through synthetic RF data, demonstrating that the fusion of monostatic and bistatic links consistently yields environment maps with higher accuracy, greater robustness and faster convergence than single-link baselines.

Problem

• ISAC
• environment mapping
• monostatic sensing
• bistatic sensing
• multipath scattering

Innovation

• ISAC
• monostatic-bistatic fusion
• multipath-based SLAM
• diffuse scattering
• Bayesian environment mapping