Complexity-Scalable Direct Geolocation and Cancellation of Terrestrial GNSS Jammers: Single-Satellite and Multi-Antenna Experiments in Low Earth Orbit

David Evans; Francesco Menzione; Giacomo Pojani; Javier Tegedor; Joaquim Fortuny-Guasch; Jonas Stene Lindbjor; Maximilian Henkel; Tim Oerther

arxiv: 2607.02190 · v1 · pith:FMRYLOLNnew · submitted 2026-07-02 · 📡 eess.SP

Complexity-Scalable Direct Geolocation and Cancellation of Terrestrial GNSS Jammers: Single-Satellite and Multi-Antenna Experiments in Low Earth Orbit

Giacomo Pojani , Javier Tegedor , Joaquim Fortuny-Guasch , Francesco Menzione , David Evans , Tim Oerther , Maximilian Henkel , Jonas Stene Lindbjor This is my paper

Pith reviewed 2026-07-03 07:41 UTC · model grok-4.3

classification 📡 eess.SP

keywords GNSS jammingquasi-direct geolocationLEO satellitesI/Q compressionRFI monitoringdelay-Doppler processingspectrum sensinginterferometry

0 comments

The pith

Quasi-direct geolocation compresses I/Q samples to locate GNSS jammers from LEO satellites at extreme ratios.

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

The paper develops quasi-direct geolocation to let small low-Earth-orbit satellites sense GNSS radio-frequency interference without needing full downlink of raw samples. It combines data compression with fast delay-Doppler matching and interferometry performed in a quantized time-frequency domain. This reduces the bandwidth and compute load that normally block spectrum monitoring on size-weight-and-power limited spacecraft. Real experiments using a repurposed GNSS reflectometry satellite collected during a 2025 jamming test show accurate jammer positions across different signal strengths. A reader would care because the approach turns existing LEO assets into contributors to near-real-time multi-constellation interference monitoring.

Core claim

Quasi-direct geolocation is an ensemble of algorithms that first compress I/Q samples, then execute fast delay-Doppler shift matching and interferometry inside a quantized time-frequency domain to locate multiple terrestrial RF sources. The method supplies mathematical precision bounds and covers both single- and multi-antenna cases. When applied to real data gathered by OPS-SAT PRETTY during Jammertest 2025, it achieves the claimed geolocation accuracy for GNSS jammers of varying SNR at extremely high compression ratios.

What carries the argument

Quasi-direct geolocation (QDG): compression of I/Q samples followed by fast delay-Doppler matching and interferometry in a quantized time-frequency domain that accelerates exhaustive search over the position domain.

If this is right

Small SWaP-limited LEO satellites become usable for GNSS RFI monitoring.
Near-real-time operation becomes feasible inside a multi-constellation interference monitoring system.
Different compression techniques can be combined while retaining geolocation performance.
Both single-satellite and multi-antenna configurations are supported with explicit precision bounds.

Where Pith is reading between the lines

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

The same compressed pipeline could be applied to other radio-frequency bands if the time-frequency quantization remains adequate.
Existing GNSS reflectometry missions could be repurposed for continuous jammer tracking without new hardware.
If compression preserves phase across successive satellite passes, multi-pass fusion would further tighten location estimates.
Downlink bandwidth savings would allow more frequent sampling windows on the same limited satellite resources.

Load-bearing premise

The chosen compression methods and quantized time-frequency processing preserve enough information to reach the stated geolocation precision despite real orbital motion and varying jammer signal strengths.

What would settle it

Processing the same real LEO I/Q recordings at the reported compression ratios and obtaining position errors that exceed the paper's derived precision bounds for any tested SNR would show the claim does not hold.

read the original abstract

Monitoring the radio-frequency (RF) spectrum from space imposes demanding requirements to satellite platforms in terms of communication bandwidth and computational resources, which are necessary for the downlink, the storage, and the processing of high-throughput I/Q samples. This paper analyzes in depth the quasi-direct geolocation (QDG) as a technique to enable the exploitation of satellites of opportunity in low Earth orbit (LEO) to sense the spectrum in the bands of global navigation satellite systems (GNSS). This is a technique of passive RF geolocation and consists of an ensemble of signal processing algorithms, which compress the I/Q samples and process the compressed data through fast delay-Doppler shift matching and interferometry in a quantized time-frequency domain. These algorithms speed up the exhaustive search of multiple RF sources in the position domain. The efficiency gain addresses the bottleneck that prevents the employment of satellites, which are limited in downlink capacity and on-board computational power. These satellites are usually constrained in size, weight and power (SWaP) and represent most of the spacecrafts in LEO. The ability to exploit assets as such for the geolocation of terrestrial GNSS jammers in near real time is instrumental the performance of a multi-constellation GNSS RFI monitoring system. The present work describes the mathematical framework and precision bounds, introduces single- and multi-antenna uses cases, combines different compression methods, and evaluates the geolocation accuracy with real data. The I/Q samples were collected by a repurposed GNSS reflectometry (GNSS-R) satellite, OPS-SAT PRETTY, in a dedicated test session during Jammertest 2025. The experimental results demonstrate the capability to geolocate GNSS jammers with different signal-to-noise ratios (SNR) with extremely high compression ratios.

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.

Desk Editor's Note private letter to a colleague

The paper brings real OPS-SAT data to compressed GNSS jammer geolocation from LEO, but the accuracy claims under quantization still need the actual numbers and error comparisons to hold up.

read the letter

The main thing to know is that this work takes actual I/Q samples collected by OPS-SAT during Jammertest 2025 and runs them through a quantized time-frequency pipeline to geolocate terrestrial GNSS jammers. They report results across different SNRs with both single- and multi-antenna setups.

What is new is the specific use of quasi-direct geolocation plus compression and interferometry for this jammer-monitoring task on a SWaP-limited LEO platform. The paper lays out the mathematical framework, derives precision bounds, combines several compression methods, and evaluates everything on the real satellite data rather than just simulations.

The experimental side is the strongest part. Using a repurposed GNSS reflectometry satellite in a dedicated test session gives the results some grounding that pure theory papers lack. The focus on downlink and compute constraints for typical LEO assets is also practical.

The soft spot is exactly the one the stress-test note flags. The abstract asserts extremely high compression ratios while retaining accuracy, yet supplies no numerical compression factors, no RMSE tables against an uncompressed baseline, and no explicit propagation of quantization error through the delay-Doppler and interferometry steps under real orbital velocity. Without those quantities it is difficult to judge whether the claimed precision survives the compression for the reported SNRs and geometries. If the full manuscript contains those comparisons and the analysis, the gap closes; otherwise the central experimental claim stays hard to verify.

This paper is for the GNSS RFI monitoring and space-based spectrum-sensing crowd. Readers who need concrete LEO data on jammer geolocation will find the experiments useful even if the compression details require more work.

It deserves a serious referee. The real-data component and the operational angle are enough to justify review time.

Referee Report

2 major / 2 minor

Summary. The paper presents the quasi-direct geolocation (QDG) technique for passive RF geolocation of terrestrial GNSS jammers using LEO satellites of opportunity. It develops a pipeline that compresses I/Q samples and performs fast delay-Doppler matching plus interferometry in a quantized time-frequency domain, derives mathematical precision bounds, and evaluates single- and multi-antenna cases. Experiments use real I/Q data collected by the OPS-SAT PRETTY GNSS-R satellite during Jammertest 2025, claiming geolocation of jammers at different SNRs with extremely high compression ratios while retaining accuracy.

Significance. If the experimental accuracy holds under the reported compression, the work would enable practical RF spectrum monitoring and jammer geolocation from SWaP-constrained LEO platforms by addressing downlink and onboard processing bottlenecks. The use of real OPS-SAT data during a dedicated test campaign provides direct evidence of applicability to multi-constellation GNSS RFI monitoring, and the combination of compression methods with interferometry offers a concrete efficiency gain over exhaustive search.

major comments (2)

[§4] §4 (Experimental Results) and associated tables/figures: the manuscript asserts 'extremely high compression ratios' and retained geolocation precision across SNR levels but supplies neither the numerical compression factors achieved nor a direct RMSE comparison of the quantized QDG pipeline against the uncompressed baseline under the actual LEO orbital velocity and geometry; without these quantities the central experimental claim cannot be verified for internal consistency.
[§3] §3 (Mathematical Framework and Precision Bounds): the error-propagation analysis through the quantized time-frequency domain is not shown explicitly; it is unclear how quantization noise and the chosen compression operators affect the delay-Doppler matching variance for the reported jammer SNRs, which is load-bearing for the claimed precision bounds.

minor comments (2)

Notation for the quantized domain operators is introduced without a dedicated nomenclature table; a short table would improve readability when multiple compression methods are combined.
Figure captions for the single- versus multi-antenna results should explicitly state the compression ratio and SNR values used in each panel.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the opportunity to clarify and strengthen the manuscript. We address each major comment below.

read point-by-point responses

Referee: [§4] §4 (Experimental Results) and associated tables/figures: the manuscript asserts 'extremely high compression ratios' and retained geolocation precision across SNR levels but supplies neither the numerical compression factors achieved nor a direct RMSE comparison of the quantized QDG pipeline against the uncompressed baseline under the actual LEO orbital velocity and geometry; without these quantities the central experimental claim cannot be verified for internal consistency.

Authors: We agree that explicit numerical compression ratios and a side-by-side RMSE comparison against the uncompressed baseline are necessary to substantiate the central claims. The experiments were performed on real OPS-SAT I/Q data collected under actual LEO geometry and velocity, so the comparison is feasible. In the revised manuscript we will add a table in §4 reporting the achieved compression ratios (with the exact factors obtained) together with RMSE values for both the quantized QDG pipeline and the uncompressed reference under the same orbital conditions and SNR levels. revision: yes
Referee: [§3] §3 (Mathematical Framework and Precision Bounds): the error-propagation analysis through the quantized time-frequency domain is not shown explicitly; it is unclear how quantization noise and the chosen compression operators affect the delay-Doppler matching variance for the reported jammer SNRs, which is load-bearing for the claimed precision bounds.

Authors: Section 3 presents the overall precision bounds, but we accept that the explicit propagation of quantization noise through the chosen compression operators and into the delay-Doppler variance is not laid out step-by-step. In the revision we will expand the mathematical framework with an additional derivation (or subsection) that isolates the contribution of quantization noise to the matching variance at the jammer SNRs used in the experiments. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental validation on independent real data with no self-referential reductions

full rationale

The provided abstract and text describe a QDG pipeline (compression, delay-Doppler matching, interferometry in quantized TF domain) whose precision bounds and accuracy are evaluated directly on real OPS-SAT I/Q samples collected during Jammertest 2025. No equations, fitted parameters, or self-citations are quoted that would make any claimed geolocation result equivalent to its inputs by construction. The work presents a mathematical framework and then tests it against external experimental data, satisfying the default expectation of a non-circular experimental paper. No load-bearing step reduces to a self-definition, a renamed fit, or an unverified author prior result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the chosen compression and quantized-domain matching preserve geolocation information; no free parameters or new entities are introduced in the abstract.

axioms (1)

domain assumption Compression and quantized time-frequency processing preserve sufficient information for accurate geolocation under real LEO conditions
Invoked to justify the efficiency gain and experimental claims (abstract, paragraphs on algorithms and results).

pith-pipeline@v0.9.1-grok · 5894 in / 1301 out tokens · 27700 ms · 2026-07-03T07:41:01.516678+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

[1]

Abdoush, Y., Pojani, G., Bartolucci, M., & Corazza, G. E. (2017). Time-frequency interference rejection based on the S -transform for GNSS applications. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 1–6. https://doi.org/10.1109/ICC.2017.7996554 Abdoush, Y., Pojani, G., & Corazza, G. E. (2019a). Adaptive inst...

work page doi:10.1109/icc.2017.7996554 2017
[2]

C., & Chan, Y

https://doi.org/10.3390/engproc2026126020 Ho, K. C., & Chan, Y. T. (1997). Geolocation of a known altitude object from TDOA and FDOA measurements. IEEE Transactions on Aerospace and Electronic Systems, 33(3), 770–783. https://doi.org/10.1109/7.599239 Ho, K. C., Lu, X., & Kovavisaruch, L. (2007). Source localization using TDOA and FDOA measurements in the ...

work page doi:10.3390/engproc2026126020 1997
[3]

J., Narula, L., Iannucci, P

https://doi.org/10.3390/engproc2025088008 Murrian, M. J., Narula, L., Iannucci, P. A., Budzien, S., O’Hanlon, B. W., Psiaki, M. L., & Humphreys, T. E. (2021). First re sults from three years of GNSS interference monitoring from low Earth orbit. NAVIGATION: Journal of the Institute of Navigation, 68(4), 673–685. https://doi.org/10.1002/navi.449 Pirat, C., ...

work page doi:10.3390/engproc2025088008 2021
[4]

Weiss, A

ESA Navigation Technology (NAVITEC), Noordwijk, Netherlands. Weiss, A. J. (2011). Direct geolocation of wideband emitters based on delay and Doppler. IEEE Transactions on Signal Processing, 59 (6), 2513 –2521. https://doi.org/10.1109/TSP.2011.2128311 Zeif, R., Hörmer, A. J., Kubicka, M., Henkel, M., & Koudelka, O. (2020). From OPS -SAT to PRETTY mission: ...

work page doi:10.1109/tsp.2011.2128311 2011

[1] [1]

Abdoush, Y., Pojani, G., Bartolucci, M., & Corazza, G. E. (2017). Time-frequency interference rejection based on the S -transform for GNSS applications. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 1–6. https://doi.org/10.1109/ICC.2017.7996554 Abdoush, Y., Pojani, G., & Corazza, G. E. (2019a). Adaptive inst...

work page doi:10.1109/icc.2017.7996554 2017

[2] [2]

C., & Chan, Y

https://doi.org/10.3390/engproc2026126020 Ho, K. C., & Chan, Y. T. (1997). Geolocation of a known altitude object from TDOA and FDOA measurements. IEEE Transactions on Aerospace and Electronic Systems, 33(3), 770–783. https://doi.org/10.1109/7.599239 Ho, K. C., Lu, X., & Kovavisaruch, L. (2007). Source localization using TDOA and FDOA measurements in the ...

work page doi:10.3390/engproc2026126020 1997

[3] [3]

J., Narula, L., Iannucci, P

https://doi.org/10.3390/engproc2025088008 Murrian, M. J., Narula, L., Iannucci, P. A., Budzien, S., O’Hanlon, B. W., Psiaki, M. L., & Humphreys, T. E. (2021). First re sults from three years of GNSS interference monitoring from low Earth orbit. NAVIGATION: Journal of the Institute of Navigation, 68(4), 673–685. https://doi.org/10.1002/navi.449 Pirat, C., ...

work page doi:10.3390/engproc2025088008 2021

[4] [4]

Weiss, A

ESA Navigation Technology (NAVITEC), Noordwijk, Netherlands. Weiss, A. J. (2011). Direct geolocation of wideband emitters based on delay and Doppler. IEEE Transactions on Signal Processing, 59 (6), 2513 –2521. https://doi.org/10.1109/TSP.2011.2128311 Zeif, R., Hörmer, A. J., Kubicka, M., Henkel, M., & Koudelka, O. (2020). From OPS -SAT to PRETTY mission: ...

work page doi:10.1109/tsp.2011.2128311 2011