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arxiv: 2604.14013 · v1 · submitted 2026-04-15 · 💻 cs.RO · cs.AI· cs.CV· eess.IV· eess.SP

Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain

Tim Hansen , Arturo Gomez-Chavez , Ilya Shimchik , Andreas Birk This is my paper

Pith reviewed 2026-05-10 13:12 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.CVeess.IVeess.SP
keywords radarfrequency domainodometrymulti-object trackingego-motioncorrelationrobustnessautonomous vehicles
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The pith

Processing radar data in the frequency domain yields higher robustness for multi-object tracking on fast-moving vehicles than feature-based methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that radar signals should be handled directly in the frequency domain to resist noise and structural errors when a vehicle moves quickly amid an unknown number of other moving objects. This matters because feature extraction often fails under high ego-motion, limiting reliable radar-only systems for tasks such as autonomous racing or highway driving. Correlation methods applied in the frequency domain also return information on every moving structure in the scene at once rather than on isolated points. Early tests with a two-dimensional Fourier approach on a public driving dataset demonstrate radar-only odometry to illustrate the point.

Core claim

Processing radar data in the frequency domain achieves higher robustness against noise and structural errors than feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. Initial experiments and results with Fourier SOFT in 2D are presented that use the Boreas dataset to demonstrate radar-only-odometry.

What carries the argument

Frequency-domain correlation applied to radar returns for registration and motion estimation, which returns information on all scene structures without prior feature selection.

If this is right

  • Radar-only odometry becomes feasible without sensor fusion even during overtaking maneuvers.
  • All moving structures contribute to the registration result rather than only extracted features.
  • Performance remains stable when the number of other moving objects is unknown.
  • The same correlation output can support both odometry and multi-object tracking.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Correlation peaks could be tracked over successive frames to estimate separate velocities for each moving object.
  • The approach may extend to other radar-based robotics tasks where feature detectors are unreliable.
  • It could lower the need for lidar or camera fusion in weather conditions that degrade those sensors.
  • Implementation on embedded hardware might be simpler than pipelines that first detect and match features.

Load-bearing premise

Frequency-domain correlation avoids the noise sensitivity and structural errors of feature extraction without introducing comparable new failure modes under real automotive dynamics.

What would settle it

A head-to-head test on high-ego-motion sequences containing multiple dynamic objects where a standard feature-based radar method achieves lower trajectory error than the frequency-domain approach.

Figures

Figures reproduced from arXiv: 2604.14013 by Andreas Birk, Arturo Gomez-Chavez, Ilya Shimchik, Tim Hansen.

Figure 1
Figure 1. Figure 1: Registration in the frequency domain, here of sonar data with FS2D, can not only deal very well with noisy data. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: Autonomous overtaking includes the perception chal [PITH_FULL_IMAGE:figures/full_fig_p002_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: The A2RL races feature fully autonomous driving at [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: The Glen Shield route from the Boreas dataset (image [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The estimated trajectories (blue) and the ground truth [PITH_FULL_IMAGE:figures/full_fig_p003_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Three examples of the FS2D registration. On the top, an overlay of two scans in red and blue is shown using their [PITH_FULL_IMAGE:figures/full_fig_p004_7.png] view at source ↗
read the original abstract

We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. A typical automotive application case is overtaking maneuvers, which in the context of autonomous racing are used here as a motivating example. Initial experiments and results with Fourier SOFT in 2D (FS2D) are presented that use the Boreas dataset to demonstrate radar-only-odometry, i.e., radar-odometry without sensor-fusion, to support our arguments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes processing automotive radar data in the frequency domain via Fourier SOFT in 2D (FS2D) to achieve higher robustness to noise and structural errors than feature-based methods, particularly under high ego-motion and scenes with unknown numbers of moving objects. It highlights that correlation-based registration inherently supplies information on all moving structures, using overtaking maneuvers in autonomous racing as motivation, and presents initial Boreas dataset experiments for radar-only odometry.

Significance. If the robustness and multi-object information advantages are demonstrated, the frequency-domain approach could meaningfully advance radar perception for dynamic, high-speed scenarios by avoiding feature-extraction failures and simplifying pipelines for odometry and tracking in autonomous driving and racing applications.

major comments (2)
  1. [Abstract] Abstract: The central claim of superior robustness against noise and structural errors (especially vs. feature-based methods under high dynamics) is asserted but the described initial experiments provide no quantitative support, baselines, error bars, or controlled tests.
  2. [Experimental evaluation] Experimental evaluation: The Boreas results demonstrate only radar-only odometry trajectory accuracy; they contain no ablation on added noise, no structural perturbation tests, no labeled multi-object dynamic scenes, and no direct comparison to feature-based registration (e.g., CFAR + ICP), so the robustness advantage remains unmeasured.
minor comments (1)
  1. The acronym expansion and precise relation of FS2D to the original SOFT method should be stated explicitly on first use for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments correctly identify that our claims regarding robustness advantages of frequency-domain processing would benefit from stronger experimental support. We address each major comment below, indicating planned revisions to better align the presentation with the initial nature of the results while defending the core methodological contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim of superior robustness against noise and structural errors (especially vs. feature-based methods under high dynamics) is asserted but the described initial experiments provide no quantitative support, baselines, error bars, or controlled tests.

    Authors: We acknowledge that the abstract asserts robustness advantages of FS2D over feature-based methods, particularly under high dynamics, while the experiments are presented as initial demonstrations of radar-only odometry on the Boreas dataset. These results show that the correlation-based registration in the frequency domain succeeds in high-ego-motion scenes with dynamic objects, avoiding feature-extraction failures that commonly affect methods like CFAR+ICP. To address the concern, we will revise the abstract to explicitly note the preliminary character of the experiments, clarify that the robustness is supported by the method's avoidance of explicit feature detection and its ability to capture all moving structures via correlation, and include error bars on the reported trajectory metrics. This revision will temper the claims without misrepresenting the work. revision: yes

  2. Referee: [Experimental evaluation] Experimental evaluation: The Boreas results demonstrate only radar-only odometry trajectory accuracy; they contain no ablation on added noise, no structural perturbation tests, no labeled multi-object dynamic scenes, and no direct comparison to feature-based registration (e.g., CFAR + ICP), so the robustness advantage remains unmeasured.

    Authors: The current experiments focus on radar-only odometry to validate FS2D feasibility in real high-dynamic driving data from Boreas, which includes overtaking and other moving objects. This establishes that frequency-domain processing enables registration without feature extraction. We agree that the absence of noise ablations, structural perturbations, labeled MOT scenes, and direct comparisons (e.g., to CFAR+ICP) means the quantitative robustness advantage is not fully measured in this initial study. In revision we will expand the experimental discussion to highlight the method's inherent properties (correlation providing multi-object information without explicit detection), report any available variation metrics from the existing trajectories, and add a dedicated subsection outlining targeted future experiments for noise robustness, perturbation tests, and multi-object tracking to substantiate the claims. revision: partial

Circularity Check

0 steps flagged

No circularity: proposal and external validation are independent of inputs

full rationale

The paper proposes frequency-domain radar processing (FS2D) as an alternative to feature-based methods, citing general advantages of correlation in the Fourier domain for robustness and multi-object awareness. It supports the claim with initial experiments on the external Boreas dataset for radar-only odometry. No equations, fitted parameters, or self-citations are presented that reduce the central robustness claim to a tautology or to the experimental inputs by construction. The derivation chain consists of a methodological suggestion plus feasibility demonstration rather than any self-referential loop.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated. The central claim implicitly assumes that frequency-domain representations preserve motion information without loss relative to spatial/feature domains.

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