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Joint Pilot and Unknown Data-based Localization for OFDM Opportunistic Radar Systems

Mathieu Reniers, Martin Willame, Jérôme Louveaux, Luc Vandendorpe

TL;DR

This work tackles target localization for OFDM opportunistic radar by jointly leveraging known pilot signals and unknown data payloads without requiring data decoding. By formulating a maximum-likelihood estimator and deriving closed-form nuisance-parameter solutions, the method combines pilot-based range information with data-driven angular information via projection operations. The approach is accelerated with FFT-based 2D/1D transforms, enabling efficient range-angle search, and it demonstrates superior localization performance over pilot-only baselines in simulations under low-SNR and low-visibility conditions. The results suggest meaningful improvements for ISAC-enabled uplink sensing, with robustness to communication performance constraints and potential extensions to distributed and multipath scenarios.

Abstract

Integrated Sensing and Communications (ISAC) has emerged as a promising paradigm for Sixth Generation (6G) and Wi-Fi 7 networks, with the communication-centric approach being particularly attractive due to its compatibility with current standards. Typical communication signals comprise both deterministic known pilot signals and random unknown data payloads. Most existing approaches either rely solely on pilots for positioning, thereby ignoring the radar information present in the received data symbols that constitute the majority of each frame, or rely on data decisions, which bounds positioning performance to that of the communication system. To overcome these limitations, we propose a novel method that extracts positioning information from data payloads without decoding them. We consider an opportunistic scenario in which communication signals from a user are captured by an opportunistic radar equipped with a Uniform Linear Arrays of antennas. We show that, in this setting, the estimation can be efficiently implemented using Fast Fourier Transforms. Finally, we demonstrate superior localization performance compared to existing methods in the literature through numerical simulations.

Joint Pilot and Unknown Data-based Localization for OFDM Opportunistic Radar Systems

TL;DR

This work tackles target localization for OFDM opportunistic radar by jointly leveraging known pilot signals and unknown data payloads without requiring data decoding. By formulating a maximum-likelihood estimator and deriving closed-form nuisance-parameter solutions, the method combines pilot-based range information with data-driven angular information via projection operations. The approach is accelerated with FFT-based 2D/1D transforms, enabling efficient range-angle search, and it demonstrates superior localization performance over pilot-only baselines in simulations under low-SNR and low-visibility conditions. The results suggest meaningful improvements for ISAC-enabled uplink sensing, with robustness to communication performance constraints and potential extensions to distributed and multipath scenarios.

Abstract

Integrated Sensing and Communications (ISAC) has emerged as a promising paradigm for Sixth Generation (6G) and Wi-Fi 7 networks, with the communication-centric approach being particularly attractive due to its compatibility with current standards. Typical communication signals comprise both deterministic known pilot signals and random unknown data payloads. Most existing approaches either rely solely on pilots for positioning, thereby ignoring the radar information present in the received data symbols that constitute the majority of each frame, or rely on data decisions, which bounds positioning performance to that of the communication system. To overcome these limitations, we propose a novel method that extracts positioning information from data payloads without decoding them. We consider an opportunistic scenario in which communication signals from a user are captured by an opportunistic radar equipped with a Uniform Linear Arrays of antennas. We show that, in this setting, the estimation can be efficiently implemented using Fast Fourier Transforms. Finally, we demonstrate superior localization performance compared to existing methods in the literature through numerical simulations.
Paper Structure (14 sections, 13 equations, 4 figures)

This paper contains 14 sections, 13 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System model
  3. Scenario
  4. Channel model
  5. Proposed method
  6. Maximum Likelihood Estimator
  7. Pilot component
  8. Data component
  9. Least Squares
  10. FFT-based Acceleration
  11. Numerical results
  12. Performance characterization
  13. Influence of system parameters
  14. Conclusion

Figures (4)

  • Figure 1: Illustration of the considered scenario. The uplink signal transmitted by the (i.e., the ) is received by the intended communication , and is simultaneously captured by a (i.e., the ).
  • Figure 2: Illustration of the considered resource grid.
  • Figure 3: HitRate and RMSE of $\sin(\widehat{\theta}_{\mathrm{s}})$ and $\widehat{R}_{\mathrm{s}}$ as a function of SNR. The HitRate is shown for $\mathrm{SNR} \in [-40dB,-18dB]$ (gray area) and the RMSE for $\mathrm{SNR} \in [-40dB,20dB]$. System parameters: $P=1$ (BPSK symbols), $D=32$, $\mathcal{C} = \{16\texttt{QAM}\}$, $Q=256$, $\Delta_f=15kHz$, $N=16$, $\Delta_d=0.5\lambda_{\mathrm{c}}$. Results are obtained for 10000 Monte-Carlo iterations.
  • Figure 4: System parameters: $P=1$ (BPSK symbols), $D=32$, $\mathcal{C} = \{16\texttt{QAM}\}$, $Q=256$ for (a), $\Delta_f=15kHz$, $\Delta_d=0.5\lambda_{\mathrm{c}}$, $N=16$ for (b). Results are obtained for 10000 Monte-Carlo iterations.