Dense Soft Weighting for Radar Ego-Velocity Estimation

• URL: http://arxiv.org/abs/2607.26980v1
• Date: Wed, 29 Jul 2026 14:37:52 GMT
• Title: Dense Soft Weighting for Radar Ego-Velocity Estimation
• Abstract summary: We present Dense Soft Weighting, an analytic radar front-end for ego-velocity estimation.<n>It maps every range-Doppler cell to a continuous confidence metric rather than enforcing a binary detection threshold.<n>It reduces mean absolute pose error by 31-45% relative to the strongest CFAR point-cloud baseline.
• Score: 6.356174487225252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sensing ego-velocity estimation is fundamental to state estimation in visually degraded environments, where camera- and LiDAR-based pipelines can become unreliable. Millimetre-wave radar is well suited to these conditions because it provides direct Doppler velocity sensing and remains robust to poor illumination, textureless scenes, and airborne particulates. However, conventional radar ego-velocity pipelines typically apply constant false alarm rate (CFAR) thresholding to convert dense radar spectra into sparse point clouds, prematurely discarding sub-threshold returns that may still retain useful Doppler motion cues. We present Dense Soft Weighting, an analytic radar front-end that maps every range-Doppler cell to a continuous confidence metric rather than enforcing a binary detection threshold. Ego-velocity is then estimated using a deterministic robust weighted least-squares formulation, while the same weighted measurements provide a closed-form, measurement-derived velocity covariance for integration with a shared inertial back-end. The method requires no platform-specific training data or learning-based uncertainty model, supporting transfer across single-chip radar configurations. Across two public datasets and one self-collected dataset, Dense Soft Weighting reduces mean absolute pose error by 31-45% relative to the strongest CFAR point-cloud baseline under an identical inertial back-end, while running in real time on embedded hardware.

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