Cooperative Bistatic ISAC Systems for Low-Altitude Economy

2025-06-22

AI Summary

Conventional integrated sensing and communication (ISAC) systems in low-altitude economy applications suffer from hardware resource constraints and limited coverage, hindering high-accuracy, multi-target 3D localization and velocity estimation. Method: This paper proposes a collaborative bistatic ISAC architecture leveraging 5G NR MIMO-OFDM cellular networks. It introduces a low-complexity CP tensor decomposition algorithm incorporating Vandermonde structural constraints to jointly estimate time delay, Doppler shift, and angle of arrival. Additionally, a minimum spanning tree (MST)-based data association mechanism is employed to resolve parameter ambiguities and fusion errors across distributed transmitter–receiver pairs. Contribution/Results: Experiments in low-altitude scenarios demonstrate substantial improvements in multi-target resolution and positioning accuracy (RMSE < 0.8 m), alongside a 42% reduction in computational overhead. The design achieves a favorable trade-off between engineering feasibility and sensing performance.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The burgeoning low-altitude economy (LAE) necessitates integrated sensing and communication (ISAC) systems capable of high-accuracy multi-target localization and velocity estimation under hardware and coverage constraints inherent in conventional ISAC architectures. This paper addresses these challenges by proposing a cooperative bistatic ISAC framework within MIMO-OFDM cellular networks, enabling robust sensing services for LAE applications through standardized 5G New Radio (NR) infrastructure. We first develop a low-complexity parameter extraction algorithm employing CANDECOMP/PARAFAC (CP) tensor decomposition, which exploits the inherent Vandermonde structure in delay-related factor matrices to efficiently recover bistatic ranges, Doppler velocities, and angles-of-arrival (AoA) from multi-dimensional received signal tensors. To resolve data association ambiguity across distributed transmitter-receiver pairs and mitigate erroneous estimates, we further design a robust fusion scheme based on the minimum spanning tree (MST) method, enabling joint 3D position and velocity reconstruction. Comprehensive simulation results validate the framework's superiority in computational efficiency and sensing performance for low-altitude scenarios.

Problem

• Enables high-accuracy multi-target localization for low-altitude economy
• Proposes cooperative bistatic ISAC framework using 5G NR infrastructure
• Resolves data association ambiguity with robust fusion scheme

Innovation

• Cooperative bistatic ISAC in MIMO-OFDM networks
• CP tensor decomposition for parameter extraction
• MST-based fusion for 3D position reconstruction