Doppler-aided Position-Velocity Tracking of Hidden Objects using mmWave Radar

Yerin Cheon; Kush Garg; Akshat Dave

cs.ROOct 25, 2026

Doppler-aided Position-Velocity Tracking of Hidden Objects using mmWave Radar

Authors: Yerin Cheon, Kush Garg, Akshat Dave

Organizations: Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, United States. · Delhi Technological University, 110042, New Delhi, India.

Abstract

Safe indoor operation requires autonomous tracking of objects that are not directly visible to the robot. Optical non-line-of-sight (NLoS) methods depend on ambient lighting and scene geometry or on weak multi-bounce returns that need long acquisitions. Millimeter-wave (mmWave) radar offers an attractive alternative: walls act as reflectors for mmWave signals, and frequency-modulated continuous-wave (FMCW) radars enable fast, practical measurement of range, angle, and velocity. However, the low resolution of these radars leads to spatially ambiguous observations. The Doppler information, although particularly sensitive to target motion, remains underexplored in indoor NLoS localization: existing methods emphasize spatial representations and either collapse or discard the Doppler dimension before learning. To leverage the complete Range-Azimuth-Doppler (RAD) information, we acquire the first indoor NLoS radar dataset that pairs sequential RAD measurements with synchronized position and velocity ground truth under controlled variations in motion: 576 sequences totaling 720,856 frames across 12 trajectories of a mobile robot moving along varying speeds, starting positions, and material configurations. We then formulate NLoS tracking as sequential position-velocity estimation rather than frame-wise localization: a shared 3-D encoder processes the full RAD representation of consecutive frames, separate recurrent branches estimate position and velocity, and post-inference refinement combines the two into a motion-consistent trajectory. Our method achieves 8.95cm position and 1.75cm/s velocity errors on held-out trajectories and generalizes to unseen scenarios involving a moving human target. Together, our dataset and tracker establish a benchmark for tracking hidden moving targets with a low-cost radar.

Figures & tables

Fig. 2 : Materials and trajectories in our dataset acquisition . Our dataset includes four target configurations and 12 trajectory patterns.

Fig. 3 : Our NLoS position-velocity tracking pipeline. Input RAD frame comprises 5 channels for each time step: normalized power, validity mask, and positional embeddings for RAD dimensions. A shared 3D residual CNN encodes the RAD frame into a 48-D feature. Independent position and velocity branches add a gated recurrent unit (GRU)- based temporal correction. The GRU hidden state is updated through eight consecutive RAD frames. The resulting position and velocity estimates are then refined for motion consistency.

Fig. 4 : Mean predicted and ground-truth position and velocity magnitudes over the validation set. Values at each time step are averaged across all validation sequences. The predictions closely follow the ground truth over time, showing that the position estimate does not drift and the velocity estimate remains accurate with time.

Speed	$e_{x}$	$e_{y}$	$e_{v_{x}}$	$e_{v_{y}}$
(m/s)	(cm)	(cm)	(cm/s)	(cm/s)
0.25	3.69	3.59	0.79	0.80
0.75	6.48	6.34	1.22	1.15
1.00	6.65	6.37	1.35	1.28

TABLE I : Mean absolute error at different target speeds. Target speed is measured using Kobuki wheel odometry and reported in m/s.

Fig. 5 : Qualitative tracking results on six distinct real-world trajectories. For each trajectory, the left panel shows the ground-truth position and color-coded velocity, while the right panel shows filtered position estimates colored by the velocity estimated by our pipeline. The results qualitatively show that the proposed method accurately estimates both position and velocity across diverse real world trajectories.

Fig. 6 : Trajectory-wise mean absolute error across different material configurations. Position errors ( $e_{x}$ , $e_{y}$ ) are reported in cm and velocity errors ( $e_{v_{x}}$ , $e_{v_{y}}$ ) in cm/s; lighter cells denote lower error. Each cell is averaged over three target speeds and four starting positions, and all errors are reported after applying motion-consistent trajectory refinement.

Configuration	Position MAE (cm)	Velocity MAE (cm/s)
Full model	8.95	1.75
w/o GRU Temporal Memory	11.16	2.10
w/o Azimuth	40.82	3.39
w/o Doppler	71.22	8.35
w/o Range	46.94	4.04

TABLE II : Ablation study of radar input dimensions and temporal memory. To ablate azimuth, Doppler, or range, we average the power and validity mask along that dimension, broadcast the mean back to the original shape, and zero the corresponding coordinate channel. To remove temporal memory, we reset both GRU hidden states before each frame.

Fig. 7 : Comparison with similar architecture but using an internal learned filter and coupled position-velocity updates. The coupled model exhibits position drift during stationary intervals, whereas our independent estimation followed by temporal refinement reduces drift. Across the full held-out N-validation set, the coupled model achieves mean position and velocity errors of $21.79$ cm and $3.27$ cm/s, compared with $8.95$ cm and $1.75$ cm/s for ours.

Fig. 8 : Evaluation of generalization across unseen targets and trajectories. A walking person (training used only the TurtleBot) on a trajectory absent from training; Our model approximately recovers the position and velocity trajectory, demonstrating good out-of-distribution generalization.

Wireless Localization

Wireless localization uses radio signals and sensor fusion to estimate the positions and trajectories of people, devices, and transmitters.

See all 65 papers

UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry
Apr 15, 2026Jui-Te Huang, Tianshu Huang, Anthony Rowe +1Radar-Inertial Odometry Robot State Estimation
Carnegie Mellon University, Pittsburgh, PA 15213, USA
Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?
Sep 17, 2026Eric Xie, Daniil Lisus, Timothy D. BarfootAutonomous Driving Perception Object Detection
Robotics Institute, University of Toronto, Canada
Dense Soft Weighting for Radar Ego-Velocity Estimation
Jul 29, 2026Atar Babgei, Chenyu Zhao, Michael Breza +1Recursive Least Squares Millimeter-Wave Radar
Imperial College London

Apr 15, 2026J/K move · Enter open · S save

UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry

Jui-Te Huang, Tianshu Huang, Anthony Rowe, Michael Kaess

Carnegie Mellon University, Pittsburgh, PA 15213, USA

Radar-Inertial Odometry Robot State Estimation mmWave Radar Sensing Uncertainty-Aware Multimodal Fusion

Open paper AskPDF arXiv

mmWave radars are robust to darkness and occlusions such as dust and smoke, and can directly constrain ego-velocity from a single frame via Doppler measurements, making them attractive sensors for odometry in visually denied conditions. However, almost all existing radar-inertial odometry systems rely on lossy radar point clouds that are highly sparse, generally concentrated in a narrow angular band, and aliased at high speeds. We propose to instead estimate ego-velocity directly from unfiltered mmWave I/Q signals. Taking advantage of a foundation model for 4D radar spectrum, we develop a system that integrates uncertainty-aware velocity predictions with IMU measurements using an uncertainty-weighted sliding-window pose graph to accurately compute odometry even when provided radar data with aliasing or an unfavorable field of view. Evaluated on public benchmark datasets, our system, UNRIO, attains the lowest relative pose error on the majority of sequences across held-out environments, despite differing chirp configurations, motion patterns, and platforms with its strongest gains coming where point clouds fail most: lateral motion in IQ1M and heavily aliased Doppler in ColoRadar.

Abstract

Wireless Localization

Explore similar work

UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry

Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?

Dense Soft Weighting for Radar Ego-Velocity Estimation