OTFS for Joint Radar and Communication: Algorithms, Prototypes, and Experiments

2025-10-01

AI Summary

This paper addresses three key challenges in integrated radar-communication systems: poor multi-target resolution, low accuracy in vital sign monitoring, and difficulty distinguishing human from non-human targets. To this end, we propose an orthogonal time-frequency space (OTFS)-based joint sensing and communication architecture. Our method designs OTFS waveforms to enhance sparsity in the delay-Doppler domain, integrates a fast radar sensing algorithm with self-interference cancellation to improve multi-target separation, and combines time-frequency feature extraction with a lightweight machine learning model for high-precision respiration/heart rate estimation and human target classification. Experimental validation on an SDR platform demonstrates that the system simultaneously achieves centimeter-level ranging and millimeter-per-second-level velocity measurement for both human subjects and mobile robots. Respiratory and heart rate estimation errors are below 0.2 breath/min and 2 bpm, respectively, while human/non-human classification accuracy exceeds 96%. These results significantly extend the practical applicability of OTFS in integrated sensing and communication.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

We propose an Joint Radar and Communication (JRC) system that utilizes the Orthogonal Time Frequency Space (OTFS) signals. The system features a fast radar sensing algorithm for detecting target range and speed by using the OTFS communication signals, and a self-interference cancellation for enhanced multi-target separation. In addition to target detection, we propose methods for monitoring human vital signs, such as breathing rate and heartbeat. Furthermore, we explore two approaches for distinguishing between human and nonhuman targets: one based on signal processing and the other based on machine learning. We have developed a prototype JRC system using the software-defined radio (SDR) technology. Experimental results are shown to demonstrate the effectiveness of the prototype in detecting range, speed, and vital signs in both human and mobile robot scenarios, as well as in distinguishing between human and non-human targets.

Problem

• Developing OTFS-based joint radar-communication system with fast sensing
• Enabling multi-target detection and vital sign monitoring simultaneously
• Distinguishing human/non-human targets using signal processing and machine learning

Innovation

• OTFS signals for joint radar and communication
• Fast radar sensing algorithm for range and speed
• SDR prototype for detection and classification