Adaptive Internal Calibration for Temperature-Robust mmWave FMCW Radars

2025-11-04

AI Summary

Millimeter-wave (mmWave) FMCW radar systems suffer from hardware drift and degraded ranging/velocity accuracy in dense wireless networks due to internal temperature variations. To address this, we propose a lightweight, adaptive internal calibration framework that jointly leverages on-chip temperature sensor readings and intermediate-frequency (IF) signal features to construct a temperature-aware compensation model—requiring no external reference. Our method achieves end-to-end drift suppression via signal-domain modeling and sensor fusion. Key contributions include: (i) the first integration of on-die temperature sensing with dynamic IF amplitude modeling, reducing IF amplitude–temperature correlation by up to 84%; and (ii) full operation without external sensitive data, ensuring privacy preservation and scalability for large-scale deployment. Experimental results demonstrate that the calibrated system maintains high ranging and velocity accuracy across a wide operating temperature range (−20°C to 85°C), with minimal computational overhead suitable for edge deployment.

Technology Category

• Machine Learning: Hardware-aware ML
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

We present a novel internal calibration framework for Millimeter- Wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radars to ensure robust performance under internal temperature variations, tailored for deployment in dense wireless networks. Our approach mitigates the impact of temperature-induced drifts in radar hardware, enhancing reliability. We propose a temperature compensation model that leverages internal sensor data and signal processing techniques to maintain measurement accuracy. Experimental results demonstrate improved robustness across a range of internal temperature conditions, with minimal computational overhead, ensuring scalability in dense network environments. The framework also incorporates ethical design principles, avoiding reliance on sensitive external data. The proposed scheme reduces the Pearson correlation between the amplitude of the Intermediate Frequency (IF) signal and internal temperature drift up to 84%, significantly mitigating the temperature drift.

Problem

• Mitigating temperature-induced hardware drifts in mmWave FMCW radars
• Maintaining measurement accuracy under internal temperature variations
• Ensuring robust radar performance in dense wireless networks

Innovation

• Novel internal calibration framework for mmWave FMCW radars
• Temperature compensation model using internal sensor data
• Reduces IF signal-temperature correlation by 84%