Kalman Filtering-Assisted Node Deployment for Distributed OTFS-ISAC: A Geometry-Aware Design for the Joint Sensing and Communication

2025-09-20

AI Summary

This paper addresses the spatial node deployment optimization problem in distributed OTFS-based integrated sensing and communication (ISAC) systems, aiming to jointly enhance target localization accuracy, velocity estimation fidelity, and communication rate while minimizing estimation errors. We propose a geometry-aware cooperative deployment framework—the first to extend orthogonal time frequency space (OTFS) modulation to distributed ISAC—and design a triangulation-based node placement mechanism, integrating delay-Doppler domain signal processing with Kalman filtering for joint sensing-communication design. A closed-form estimation error model is derived to characterize the fundamental impact of geometric deployment configurations on system performance. Furthermore, an orthogonal axial deployment algorithm is introduced to enable synergistic active and passive sensing. Experimental results demonstrate that the proposed approach significantly reduces both localization error and bit error rate, achieving an effective trade-off between sensing accuracy and communication reliability across diverse network topologies.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• 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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Integrated sensing and communication (ISAC) is a key enabler for next-generation wireless networks, offering spectrum efficiency and reduced hardware complexity. While monostatic ISAC has been well studied, its limited spatial diversity reduces reliability in high-mobility scenarios. Distributed ISAC alleviates this via cooperative nodes, but conventional OFDM-based designs remain vulnerable to Doppler shifts and multipath fading. Orthogonal time frequency space (OTFS) modulation has recently emerged as a resilient alternative, as its delay-Doppler domain representation enables robust communication and high-resolution sensing. Motivated by this, we extend OTFS to distributed ISAC and address the underexplored problem of spatial node deployment. We propose a triangulation-based framework that leverages spatial diversity to improve target localization, velocity estimation, and communication rates, and analytically characterize the role of deployment geometry in minimizing estimation error. Furthermore, we integrate Kalman filtering (KF) into distributed OTFS-ISAC to enhance tracking of moving targets, and design novel algorithms for active sensing, passive sensing, and joint sensing-communication. Closed-form expressions are derived for localization error under general topologies, and a near-optimal deployment strategy is identified by aligning receivers along orthogonal axes. Numerical evaluations show significant reductions in localization error and bit error rate (BER), while capturing the trade-offs between sensing accuracy and communication reliability. These results highlight the potential of KF-assisted node placement in distributed OTFS-ISAC for reliable, high-performance operation in dynamic wireless environments.

Problem

• Optimizing spatial node deployment for distributed OTFS-ISAC systems
• Enhancing target localization and velocity estimation in high-mobility scenarios
• Improving reliability against Doppler shifts and multipath fading effects

Innovation

• Geometry-aware node deployment using triangulation
• Kalman filtering integration for moving target tracking
• Orthogonal receiver axis alignment for error minimization