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

TL;DR

This paper introduces a Bayesian multipath-based environment mapping framework for ISAC that fuses monostatic and bistatic RF measurements, improving accuracy and robustness in low-altitude environments with non-ideal surfaces.

Contribution

It is the first to integrate monostatic and bistatic sensing in a unified Bayesian framework for environment mapping under realistic surface conditions.

Findings

Fusion improves map accuracy over single-link methods.

The framework demonstrates higher robustness in complex environments.

Faster convergence compared to traditional approaches.

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.