RadarTrack: Enhancing Ego-Vehicle Speed Estimation with Single-chip mmWave Radar
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866908328432500736 |
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| author | Sen, Argha Chakraborty, Soham Tripathy, Soham Chakraborty, Sandip |
| author_facet | Sen, Argha Chakraborty, Soham Tripathy, Soham Chakraborty, Sandip |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RadarTrack: Enhancing Ego-Vehicle Speed Estimation with Single-chip mmWave Radar Sen, Argha Chakraborty, Soham Tripathy, Soham Chakraborty, Sandip Robotics 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. |
| title | RadarTrack: Enhancing Ego-Vehicle Speed Estimation with Single-chip mmWave Radar |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.14495 |