publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2025

RadarTrack: Enhancing Ego-Vehicle Speed Estimation with Single-chip mmWave Radar

Soham Chakraborty^*, Argha Sen^*, Soham Tripathy^*, and 1 more author

2025

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In this work, we introduce RadarTrack, an innovative ego-speed estimation framework utilizing a single-chip millimeter-wave (mmWave) radar to deliver robust speed estimation for mobile platforms. Unlike previous methods that depend on cross-modal learning and computationally intensive Deep Neural Networks (DNNs), RadarTrack utilizes a novel phase-based speed estimation approach. This method effectively overcomes the limitations of conventional ego-speed estimation approaches which rely on doppler measurements and static surrondings. RadarTrack is designed for low-latency operation on embedded platforms, making it suitable for real-time applications where speed and efficiency are critical. Our key contributions include the introduction of a novel phase-based speed estimation technique solely based on signal processing and the implementation of a real-time prototype validated through extensive real-world evaluations. By providing a reliable and lightweight solution for ego-speed estimation, RadarTrack holds significant potential for a wide range of applications, including micro-robotics, augmented reality, and autonomous navigation.

2024

Poster: Dynamic Ego-Velocity Estimation Using Moving mmWave Radar: A Phase-Based Approach

Soham Chakraborty^*, Argha Sen^*, Soham Tripathy^*, and 1 more author

In Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services, Minato-ku, Tokyo, Japan, 2024

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Precise ego-motion measurement is crucial for various applications, including robotics, augmented reality, and autonomous navigation. In this poster, we propose mmPhase, an odometry framework based on single-chip millimetre-wave (mmWave) radar for robust ego-motion estimation in mobile platforms without requiring additional modalities like the visual, wheel, or inertial odometry. mmPhase leverages a phase-based velocity estimation approach to overcome the limitations of conventional doppler resolution. For real-world evaluations of mmPhase we have developed an ego-vehicle prototype. Compared to the state-of-the-art baselines, mmPhase shows superior performance in ego-velocity estimation.