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arxiv: 2605.23561 · v1 · pith:3JQQNE5Xnew · submitted 2026-05-22 · 📡 eess.SP

Reliable UAV Detection with ISAC

Stephan Saur , Mark Doll , Artjom Grudnitsky , Silvio Mandelli , Lucas Giroto , Marcus Henninger , Thorsten Wild This is my paper

Pith reviewed 2026-05-25 03:19 UTC · model grok-4.3

classification 📡 eess.SP
keywords UAV detectionISAC5GOFDM radarmono-static radarclutterintegrated sensing and communicationexperimental validation
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The pith

Unmodified commercial 5G hardware detects small UAVs with sub-meter accuracy beyond 500 meters in strong clutter.

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

The paper presents field experiments showing that mono-static OFDM radar implemented on standard 5G equipment can detect a small UAV reliably. Detection with sub-meter range accuracy holds at distances over 500 meters even when the radio environment contains strong clutter. Results are benchmarked against models that incorporate link budget calculations and hardware impairments. A sympathetic reader would care because this suggests UAV detection can be added to existing 5G-Advanced and 6G networks as an integrated sensing function without dedicated radar hardware.

Core claim

The central claim is that reliable UAV detection with sub-meter accuracy is still possible in over 500 meters distance in a challenging radio environment rich of strong clutter when using unmodified commercial 5G hardware for mono-static OFDM radar, and that measured performance aligns with expectations from link budget and hardware impairment models.

What carries the argument

Mono-static Orthogonal Frequency-Division Multiplexing (OFDM) radar on commercial 5G hardware, which reuses the communication waveform for sensing while handling both functions simultaneously.

If this is right

  • UAV detection becomes a practical use case for ISAC systems in 5G-Advanced and 6G without requiring new dedicated sensing hardware.
  • Performance predictions from link budget and impairment models remain accurate enough to guide system design in cluttered settings.
  • Sub-meter range accuracy is attainable at long range even when clutter returns are strong.
  • Existing commercial base stations can support both communication and sensing tasks in the same spectrum.

Where Pith is reading between the lines

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

  • Operators could deploy UAV monitoring as an overlay on current 5G infrastructure, lowering the cost of additional sensing services.
  • The same approach might extend to detecting other low-altitude objects such as birds or drones in urban or rural settings.
  • Further tests could examine how performance changes when multiple UAVs are present or when the UAV speed varies significantly.

Load-bearing premise

The chosen UAV flight path, speed, radar cross-section, and specific clutter environment are representative enough that the observed reliability generalizes beyond this single experiment.

What would settle it

A repeat experiment in a different clutter environment or with a UAV of different size and trajectory that fails to achieve sub-meter accuracy above 500 meters would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.23561 by Artjom Grudnitsky, Lucas Giroto, Marcus Henninger, Mark Doll, Silvio Mandelli, Stephan Saur, Thorsten Wild.

Figure 1
Figure 1. Figure 1: ISAC PoC processing flow diagram. proof-of-concept (PoC) in a realistic outdoor scenario, (ii) derivation of a link budget model considering the impairments of commercial 5G hardware, and (iii) experimental validation of the theoretical limitations. The main challenge addressed is detecting UAVs with small radar cross section (RCS) in the presence of clutter, i. e., strong unwanted signal components reflec… view at source ↗
Figure 2
Figure 2. Figure 2: ISAC PoC deployment and UAV used in sensing experiments. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Investigated flight routes (red and light blue trajectory), [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Measured target range from ISAC (×) vs. recorded GNSS coordinates of the UAV ( ) for the first experiment [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Measured target range from ISAC (×) vs. recorded GNSS coordinates of the UAV ( ) for the second experiment. is to detect the UAV in spite of the long distance to the RUs. In this scenario, ECA-C is the more reliable method for clutter removal. Reason is that static clutter and its impulsive sidelobes are the predominant components in the reflected signal and must be suppressed to allow for the detection of… view at source ↗
Figure 7
Figure 7. Figure 7: Measured target SINR vs. range from ISAC for the second [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
read the original abstract

Unmanned Aerial Vehicle (UAV) detection is one prominent use case of Integrated Sensing and Communication (ISAC) systems in 5G-Advanced and future 6G networks. In this paper, we present experimental results for the detection of a small UAV using unmodified commercial 5G hardware for mono-static Orthogonal Frequency-Division Multiplexing (OFDM) radar and compare them with the expected performance based on models for link budget and hardware impairments. We show that reliable detection with sub-meter accuracy is still possible in over 500 meters distance in a challenging radio environment rich of strong clutter.

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 presents experimental results for mono-static OFDM radar-based detection of a small UAV using unmodified commercial 5G hardware. Measurements are compared against link-budget and hardware-impairment models, with the central claim that reliable detection achieving sub-meter accuracy remains feasible at ranges exceeding 500 m in a radio environment containing strong clutter.

Significance. If the experimental support is strengthened, the work would provide concrete evidence that existing 5G infrastructure can be repurposed for practical ISAC sensing of UAVs under realistic clutter conditions, directly informing 5G-Advanced and 6G system design.

major comments (2)
  1. [Results section] Results section (and abstract): the text states that measured results are compared to link-budget and impairment models but supplies no quantitative error metrics (e.g., range RMSE or detection-probability deviation), no count of independent trials or flight repetitions, and no statistical description of the clutter environment (e.g., clutter power distribution or RCS statistics). This absence prevents assessment of whether the reported sub-meter accuracy and reliability are robustly supported.
  2. [Abstract and results discussion] Abstract and results discussion: the headline claim of reliability 'in a challenging radio environment rich of strong clutter' rests on data from a single UAV flight path, speed, and RCS realization in one specific clutter scene. The manuscript does not provide evidence or analysis showing that this trajectory and environment are representative, nor does it quantify sensitivity to variations in multipath, RCS, or clutter statistics that would be needed to support generalization.
minor comments (1)
  1. Notation for range resolution and Doppler processing should be defined explicitly when first introduced, as the OFDM radar formulation is central to the comparison with models.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our experimental demonstration of UAV detection using commercial 5G hardware. We address each major comment below, indicating revisions where appropriate.

read point-by-point responses

  1. Referee: [Results section] Results section (and abstract): the text states that measured results are compared to link-budget and impairment models but supplies no quantitative error metrics (e.g., range RMSE or detection-probability deviation), no count of independent trials or flight repetitions, and no statistical description of the clutter environment (e.g., clutter power distribution or RCS statistics). This absence prevents assessment of whether the reported sub-meter accuracy and reliability are robustly supported.

    Authors: We agree that the manuscript would be strengthened by explicit quantitative metrics. In the revised version we will compute and report range RMSE between measured and model-predicted detections, state the exact number of independent flight repetitions performed, and add a statistical summary of the clutter environment (including empirical distributions of clutter power and estimated RCS values extracted from the recorded waveforms). revision: yes

  2. Referee: [Abstract and results discussion] Abstract and results discussion: the headline claim of reliability 'in a challenging radio environment rich of strong clutter' rests on data from a single UAV flight path, speed, and RCS realization in one specific clutter scene. The manuscript does not provide evidence or analysis showing that this trajectory and environment are representative, nor does it quantify sensitivity to variations in multipath, RCS, or clutter statistics that would be needed to support generalization.

    Authors: The reported measurements were obtained from a single but carefully selected flight trajectory through a dense urban clutter scene chosen to stress the system. We accept that this does not constitute a statistical survey across multiple environments. In the revision we will add an explicit limitations paragraph stating that the results constitute a proof-of-concept demonstration rather than a comprehensive sensitivity study, and we will refrain from claiming broad generalization without further data. revision: partial

standing simulated objections not resolved
  • Full quantification of sensitivity to variations in multipath, RCS, and clutter statistics would require additional experimental campaigns that are outside the scope of the present study.

Circularity Check

0 steps flagged

No circularity: experimental measurements compared to independent models

full rationale

The paper reports direct experimental results from mono-static OFDM radar using unmodified commercial 5G hardware, with detection performance (sub-meter accuracy at >500 m) measured in a specific UAV trajectory and clutter environment. These are compared against separate models for link budget and hardware impairments that are not fitted to the presented data. No derivation chain, equation, or self-citation reduces the central claim to a fitted input or prior result by construction. The work is self-contained against external benchmarks (measured vs. modeled performance) and contains no load-bearing self-citations or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the representativeness of the single outdoor trial and on the accuracy of the link-budget and hardware-impairment models used for comparison. No free parameters, axioms, or invented entities are introduced in the abstract.

pith-pipeline@v0.9.0 · 5635 in / 1000 out tokens · 32758 ms · 2026-05-25T03:19:19.591660+00:00 · methodology

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Reference graph

Works this paper leans on

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