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Cooperative Bistatic ISAC Systems for Low-Altitude Economy

Zhenkun Zhang, Yining Xu, Cunhua Pan, Hong Ren, Qixuan Zhang, Songtao Gao, Jiangzhou Wang

TL;DR

This work tackles high-accuracy sensing in the low-altitude economy by introducing a cooperative bistatic ISAC design within MIMO-OFDM cellular networks that aligns with 5G NR. It develops a low-complexity, ESPRIT-inspired CP tensor decomposition to jointly estimate bistatic ranges, Doppler velocities, and AoAs from multi-dimensional echoes, and couples it with an MST-based fusion to resolve data associations across distributed BS pairs for robust 3D localization and velocity estimation. Extensive simulations demonstrate millimeter-level range accuracy and decimeter-level localization, with strong scalability as the network grows and in dense UAV-like target environments. The framework thus offers a practical, hardware-friendly pathway to integrated sensing and communication in next-generation cellular networks for LAE applications.

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.

Cooperative Bistatic ISAC Systems for Low-Altitude Economy

TL;DR

This work tackles high-accuracy sensing in the low-altitude economy by introducing a cooperative bistatic ISAC design within MIMO-OFDM cellular networks that aligns with 5G NR. It develops a low-complexity, ESPRIT-inspired CP tensor decomposition to jointly estimate bistatic ranges, Doppler velocities, and AoAs from multi-dimensional echoes, and couples it with an MST-based fusion to resolve data associations across distributed BS pairs for robust 3D localization and velocity estimation. Extensive simulations demonstrate millimeter-level range accuracy and decimeter-level localization, with strong scalability as the network grows and in dense UAV-like target environments. The framework thus offers a practical, hardware-friendly pathway to integrated sensing and communication in next-generation cellular networks for LAE applications.

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.

Paper Structure

This paper contains 22 sections, 1 theorem, 72 equations, 10 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Signal Transmission Model
  4. Beanforming for Low-altitude Targets
  5. Sensing Channel Model
  6. Tensor-Based Parameter Estimation Algorithm
  7. Tensor Formulation of the Received Signal
  8. Decomposition-Based Factor Matrices Recovery
  9. Parameter Estimation
  10. Uniqueness Analysis
  11. Algorithm Development
  12. Position and Velocity Estimation
  13. Basic Target Localization
  14. Error Elimination and Data Association
  15. Data Fusion for Target Localization
  16. ...and 7 more sections

Key Result

Lemma 1

Let $k(\mathbf{A})$ denote the Kruskal-rank of matrix $\mathbf{A}$. The tensor decomposition of $\mathcal{X}$ is unique if the following joint rank conditions hold: where $\mathbf{A}^{(L_1,3)}$ represents the first $L_1$ rows of $\mathbf{A}^{(3)}$, and $\mathbf{A}^{(L_2,3)}$ denotes the first $L_2$ rows of $\mathbf{A}^{(2)}$. These conditions are generically true if

Figures (10)

  • Figure 1: An illustration of the MIMO-OFDM cellular network-based bistatic ISAC architecture.
  • Figure 2: An example of feasible slot format configuration, where a duration of $N=8$ symbols can be used for sensing signal.
  • Figure 3: Radiation patterns achieved by random beamforming and the exemplary design for low-altitude target sensing.
  • Figure 4: An illustration of the MST-based error elimination and data association process, where the numbers of targets and tBS-rBS pairs are $K=3$ and $N_{\mathrm{t}} N_{\mathrm{r}}=3$, receptively.
  • Figure 5: Illustration of the system model in simulation.
  • ...and 5 more figures

Theorems & Definitions (3)

  • Remark 1
  • Lemma 1
  • Remark 2