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Vehicular Multistatic OTFS-ISAC: A Geometry-Aware Deployment and Kalman-Based Tracking

Jyotsna Rani, Kuntal Deka, Ganesh Prasad, Zilong Liu

TL;DR

This work addresses the challenge of robust joint sensing and communication in high-m mobility vehicular networks by proposing a geometry-aware multistatic OTFS-ISAC framework. It develops a triangulation-based cooperative sensing approach, derives closed-form localization-error covariances, and shows that maximizing triangulation area minimizes estimation error, with orthogonal-axis deployments achieving near-optimal cubic reductions when employing $N_a$ independent antennas. The paper also integrates a correlated random walk (CRW) based Kalman filter to enhance tracking and introduces sensing-aided channel reconstruction for reliable DD-domain sensing and communication. Numerical results demonstrate substantial localization RMSE and BER improvements, confirming the benefits of geometry-aware deployment and KF-assisted tracking for dynamic vehicular ISAC. The proposed framework thus offers a principled design and practical validation for high-mobility ISAC systems in distributed vehicular networks.

Abstract

Integrated sensing and communication (ISAC) is a promising paradigm for next-generation vehicular networks, yet existing orthogonal frequency-division multiplexing (OFDM)-based designs suffer from limited spatial diversity and severe sensitivity to Doppler and multipath effects. While orthogonal time-frequency space (OTFS) modulation offers robustness under high mobility, the impact of spatial node deployment in multistatic OTFS-ISAC has remained largely unexplored. This paper presents the first geometry-aware multistatic OTFS-ISAC framework, in which a triangulation-based cooperative sensing approach is developed for joint target localization and velocity estimation. Closed-form expressions for the localization error covariance are derived under general receiver topologies, revealing that maximizing the triangulation area is fundamental to minimizing estimation error. This leads to a near-optimal deployment strategy based on orthogonal receiver placement and its equivalence to multi-antenna architectures with cubic-order error reduction. To enable reliable tracking of moving targets, a correlated random walk (CRW)-based Kalman filter (KF) framework is integrated into multistatic OTFS-ISAC for active sensing and ISAC. Numerical results demonstrate significant reductions in localization root-mean-square error (RMSE) and communication bit error rate (BER), highlighting the effectiveness of geometry-aware, KF-assisted multistatic OTFS-ISAC in dynamic vehicular environments.

Vehicular Multistatic OTFS-ISAC: A Geometry-Aware Deployment and Kalman-Based Tracking

TL;DR

This work addresses the challenge of robust joint sensing and communication in high-m mobility vehicular networks by proposing a geometry-aware multistatic OTFS-ISAC framework. It develops a triangulation-based cooperative sensing approach, derives closed-form localization-error covariances, and shows that maximizing triangulation area minimizes estimation error, with orthogonal-axis deployments achieving near-optimal cubic reductions when employing independent antennas. The paper also integrates a correlated random walk (CRW) based Kalman filter to enhance tracking and introduces sensing-aided channel reconstruction for reliable DD-domain sensing and communication. Numerical results demonstrate substantial localization RMSE and BER improvements, confirming the benefits of geometry-aware deployment and KF-assisted tracking for dynamic vehicular ISAC. The proposed framework thus offers a principled design and practical validation for high-mobility ISAC systems in distributed vehicular networks.

Abstract

Integrated sensing and communication (ISAC) is a promising paradigm for next-generation vehicular networks, yet existing orthogonal frequency-division multiplexing (OFDM)-based designs suffer from limited spatial diversity and severe sensitivity to Doppler and multipath effects. While orthogonal time-frequency space (OTFS) modulation offers robustness under high mobility, the impact of spatial node deployment in multistatic OTFS-ISAC has remained largely unexplored. This paper presents the first geometry-aware multistatic OTFS-ISAC framework, in which a triangulation-based cooperative sensing approach is developed for joint target localization and velocity estimation. Closed-form expressions for the localization error covariance are derived under general receiver topologies, revealing that maximizing the triangulation area is fundamental to minimizing estimation error. This leads to a near-optimal deployment strategy based on orthogonal receiver placement and its equivalence to multi-antenna architectures with cubic-order error reduction. To enable reliable tracking of moving targets, a correlated random walk (CRW)-based Kalman filter (KF) framework is integrated into multistatic OTFS-ISAC for active sensing and ISAC. Numerical results demonstrate significant reductions in localization root-mean-square error (RMSE) and communication bit error rate (BER), highlighting the effectiveness of geometry-aware, KF-assisted multistatic OTFS-ISAC in dynamic vehicular environments.

Paper Structure

This paper contains 24 sections, 5 theorems, 34 equations, 6 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. OTFS-Based ISAC
  4. Monostatic ISAC
  5. Multistatic ISAC
  6. Motivation and Key Contributions
  7. System Architecture
  8. Topology
  9. Transmit OTFS Signal
  10. Received Signal Processing
  11. Active Sensing Procedure
  12. ISAC Procedure
  13. Targets Localization Via Cooperative Sensing
  14. Targets' Location
  15. Targets' Velocity
  16. ...and 9 more sections

Key Result

Lemma 1

Consider the localization of the $i$th target $\mathcal{T}_i$ via geometrical triangulation using the AN $\mathcal{A}_0$ and two receivers $\mathcal{R}_j$ and $\mathcal{R}_k$. For given variances $\{\sigma_{i,q}^2\}$ of the squared range estimates $\{\widehat{\rho}_{i,q}^2| q \in \{0,j,k\}\}$ from t

Figures (6)

  • Figure 1: Network topology for multistatic ISAC.
  • Figure 2: Estimation of location and velocity of a target via cooperative sensing.
  • Figure 3: RMSE improvement with SNR for different numbers of targets.
  • Figure 4: RMSE improvement with number of antennas per node, $N_A$ for different numbers of targets, $N_T$.
  • Figure 5: Performance of ISAC in the localization and the data rate estimation.
  • ...and 1 more figures

Theorems & Definitions (10)

  • Lemma 1
  • proof
  • Corollary 1
  • proof
  • Lemma 2
  • proof
  • Lemma 3
  • proof
  • Lemma 4
  • proof