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Integrating Low-Altitude SAR Imaging into UAV Data Backhaul

Zhen Du, Fan Liu, Jie Yang, Yuanhao Cui, Rui Wang, Zenghui Zhang, Shi Jin

Abstract

Synthetic aperture radar (SAR) on unmanned aerial vehicles (UAVs) enables high-resolution sensing in low-altitude wireless networks, while requiring reliable uplink data backhaul to ground base stations under dynamic channel conditions. Conventional orthogonal frequency division multiplexing (OFDM)-based SAR systems rely on pilot or deterministic signaling, which occupies only a small fraction of the available timefrequency (TF) resources and limits imaging performance. This paper develops a data-aided OFDM-SAR imaging framework that reuses uplink communication data symbols for sensing, thereby exploiting the dominant TF resources of the UAV backhaul link. However, the randomness of data symbols disrupts the coherent structure required for SAR imaging, especially in highly dynamic channels with strong TF coupling, leading to severe degradation in range-Doppler focusing. To address this issue, we establish a unified TF domain filtering framework to suppress data-induced randomness and recover an equivalent deterministic imaging channel. Within this framework, reciprocal, matched, and Wiener filtering are interpreted under a common formulation, enabling a systematic characterization of their impact on imaging performance. A normalized mean square error (NMSE) metric of a reference point target's profile is further adopted to quantify the joint effects of randomnessinduced distortion and noise amplification. Simulation results based on 5G NR parameters show that the proposed dataaided scheme significantly outperforms pilot-only approaches by leveraging uplink data resources, demonstrating that effective TF-domain filtering is essential to ensure high-resolution imaging in dynamic UAV channels.

Integrating Low-Altitude SAR Imaging into UAV Data Backhaul

Abstract

Synthetic aperture radar (SAR) on unmanned aerial vehicles (UAVs) enables high-resolution sensing in low-altitude wireless networks, while requiring reliable uplink data backhaul to ground base stations under dynamic channel conditions. Conventional orthogonal frequency division multiplexing (OFDM)-based SAR systems rely on pilot or deterministic signaling, which occupies only a small fraction of the available timefrequency (TF) resources and limits imaging performance. This paper develops a data-aided OFDM-SAR imaging framework that reuses uplink communication data symbols for sensing, thereby exploiting the dominant TF resources of the UAV backhaul link. However, the randomness of data symbols disrupts the coherent structure required for SAR imaging, especially in highly dynamic channels with strong TF coupling, leading to severe degradation in range-Doppler focusing. To address this issue, we establish a unified TF domain filtering framework to suppress data-induced randomness and recover an equivalent deterministic imaging channel. Within this framework, reciprocal, matched, and Wiener filtering are interpreted under a common formulation, enabling a systematic characterization of their impact on imaging performance. A normalized mean square error (NMSE) metric of a reference point target's profile is further adopted to quantify the joint effects of randomnessinduced distortion and noise amplification. Simulation results based on 5G NR parameters show that the proposed dataaided scheme significantly outperforms pilot-only approaches by leveraging uplink data resources, demonstrating that effective TF-domain filtering is essential to ensure high-resolution imaging in dynamic UAV channels.
Paper Structure (27 sections, 2 theorems, 65 equations, 8 figures)

This paper contains 27 sections, 2 theorems, 65 equations, 8 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. SAR Geometry
  4. Transmit ISAC Signal Model
  5. SAR Echo Model
  6. SAR Imaging Approach
  7. TF Filtering
  8. Reciprocal Filtering (RF)
  9. Matched Filtering (MF)
  10. Wiener Filtering (WF)
  11. Range Compression
  12. Azimuth FFT
  13. RCMC
  14. Azimuth Compression
  15. Mapping Mechanism Between Data Randomness and SAR Imaging
  16. ...and 12 more sections

Key Result

Lemma 1

The azimuth envelop $r_m\left(k-k_{q,m}\right)$ varies much slower than the phase of $e^{-j\frac{4\pi}{c} f_c\Delta R_{q,m}}$. $\blacktriangleleft$$\blacktriangleleft$

Figures (8)

  • Figure 1: Joint low-altitude OFDM-SAR imaging and data backhaul.
  • Figure 2: Technical roadmap.
  • Figure 3: OFDM-SAR imaging performance: a case of WF and $\mathrm{SNR_{in}}=-20$ dB.
  • Figure 4: Range and azimuth results of SAR imaging for different TF-domain filtering schemes under different input SNR values.
  • Figure 5: NMSE versus $\text{SNR}_\text{in}$ with different TF-domain filtering schemes.
  • ...and 3 more figures

Theorems & Definitions (8)

  • Remark 1
  • Remark 2
  • Remark 3
  • Lemma 1
  • Theorem 1
  • Remark 4
  • Remark 5
  • Remark 6