IP Library Granted Patent US 12,278,667
Granted Patent B2
US 12,278,667 · App. 17/871,293 · Granted Apr 15, 2025

Radio-frequency signal processing systems and methods

Inventors: Alexander Wulff (New York, NY); Benjamin Harpe (New York, NY); Isaac Struhl (New York, NY)
Assignee: Distributed Spectrum Inc.
H04B17/102G01S5/02213H04B7/15H04B17/13H04B17/18H04B17/3911H04B17/3913H04W4/38G06F18/214H04B1/0003H04B17/27H04L27/0002
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Quick Facts
Patent No.
US 12,278,667
App. No.
17/871,293
Granted
Apr 15, 2025
Kind
B2
Abstract

The present disclosure provides radio-frequency (RF) systems that can detect the presence of RF signals received by the system, as well as determine characteristics such as the operating frequency of RF signals, the type of RF source that transmitted each RF signal, and/or the location of each RF source with high precision and sensitivity while using low cost, scalable electronics that are versatile enough for deployment in a variety of environments. Such systems can employ a network of RF sensors that can coordinate in response to communication with a computer to perform any such detection and/or determination using trained models executed onboard the RF sensors and/or the computer. RF signals may have unique characteristics when received at one or more RF sensors that may be detected using trained models described herein, even in high noise or non-line of sight (LOS) environments and with low cost, low resolution RF receiver hardware.

Claims (50)

1. A radio-frequency (RF) source determination system, comprising:

a processor operatively coupled to a memory and configured to, in response to receiving RF characteristic data from a first RF sensor, the RF characteristic data indicating that an RF source is present in an operating environment of the RF source determination system:

instruct a second RF sensor, over a communication network, to provide RF radiation data indicating a subset of RF radiation received by the second RF sensor, the subset of RF radiation corresponding, at least in part, to an RF signal received from the RF source; and

receive the RF radiation data from the second RF sensor.

2. The RF source determination system of claim 1 , wherein the RF radiation data comprises digital samples of the subset of RF radiation received by the second RF sensor.

3. The RF source determination system of claim 2 , wherein the digital samples comprise demodulated samples.

4. The RF source determination system of claim 2 , wherein the processor is configured to instruct the second RF sensor to select the digital samples corresponding, at least in part, to a time period of reception, frequency range, and/or power level of the RF signal from the RF source.

5. The RF source determination system of claim 2 , wherein the processor is configured to instruct the second RF sensor to select the digital samples from among digital samples previously received and stored in a memory of the first RF sensor.

6. The RF source determination system of claim 2 , wherein the processor is configured to instruct the second RF sensor to select the digital samples from among digital samples received following the instruction.

7. The RF source determination system of claim 1 , wherein the processor is configured to determine that the RF source is present in the operating environment based on the RF characteristic data from the first RF sensor.

8. The RF source determination system of claim 7 , wherein the processor is configured to:

input the RF characteristic data to a trained model; and

determine that the RF source is present in the operating environment based on an output of the trained model generated in response to receiving the RF characteristic data as input.

9. The RF source determination system of claim 1 , wherein the RF characteristic data indicates the RF signal.

10. The RF source determination system of claim 1 , wherein the processor is configured to, by instructing the second RF sensor, override selection of the subset of RF radiation by the second RF sensor.

11. The RF source determination system of claim 1 , wherein the processor is configured to determine a location of the RF source in the operating environment based on the RF characteristic data from the first RF sensor and second the RF radiation data from the second RF sensor.

12. The RF source determination system of claim 1 , further comprising:

a third RF sensor, comprising:

an RF antenna;

the processor; and

the memory.

13. A method, comprising:

in response to receiving RF characteristic data from a first RF sensor, the RF characteristic data indicating that an RF source is present in an operating environment of an RF source determination system:

instructing a second RF sensor, by a processor over a communication network, to provide RF radiation data indicating a subset of RF radiation received by the second RF sensor, the subset of RF radiation corresponding, at least in part, to an RF signal received from the RF source; and

receiving, by the processor, the RF radiation data from the second RF sensor.

14. The method of claim 13 , wherein the RF radiation data comprises digital samples of the subset of RF radiation received by the second RF sensor.

15. The method of claim 14 , wherein the digital samples comprise demodulated samples.

16. The method of claim 14 , wherein instructing the second RF sensor comprises instructing the secondRF sensor to select the digital samples corresponding, at least in part, to a time period of reception, frequency range, and/or power level of the RF signal from the RF source.

17. The method of claim 14 , wherein instructing the second RF sensor comprises instructing the second RF sensor to select the digital samples from among digital samples previously received and stored in a memory of the second RF sensor.

18. The method of claim 14 , wherein instructing the second RF sensor comprises instructing the second RF sensor to select the digital samples from among digital samples received following the instruction.

19. The method of claim 13 , further comprising determining, by the processor, that the RF source is present in the operating environment based on the RF characteristic data from the first RF sensor.

20. The method of claim 19 , further comprising, by the processor:

inputting the RF characteristic data to a trained model,

wherein determining that the RF source is present in the operating environment is based on an output of the trained model generated in response to receiving the RF characteristic data as input.

21. The method of claim 13 , wherein the RF characteristic data indicates the RF signal.

22. The method of claim 15 , wherein instructing the second RF sensor comprises overriding selection of the subset of RF radiation by the second RF sensor.

23. The method of claim 15 , further comprising, by the processor, determining a location of the RF source in the operating environment based on the RF characteristic data from the first RF sensor and the RF radiation data from the second RF sensor.

24. The method of claim 13 , wherein:

a third RF sensor comprises an RF antenna and the processor.

25. A non-transitory computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to perform a method, the method comprising:

in response to receiving RF characteristic data from a first RF sensor, the RF characteristic data indicating that an RF source is present in an operating environment of an RF source determination system:

instructing a second RF sensor, over a communication network, to provide RF radiation data indicating a subset of RF radiation received by the second RF sensor, the subset of RF radiation corresponding, at least in part, to an RF signal received from the RF source; and

receiving, by the processor, the RF radiation data from the second RF sensor.

26. The non-transitory computer-readable medium of claim 25 , wherein the RF radiation data comprises digital samples of the subset of RF radiation received by the second RF sensor.

27. The non-transitory computer-readable medium of claim 26 , wherein instructing the second RF sensor comprises instructing the second RF sensor to select the digital samples from among digital samples previously received and stored in a memory of the second RF sensor.

28. The non-transitory computer-readable medium of claim 25 , wherein the method further comprises determining that the RF source is present in the operating environment based on the RF characteristic data from the first RF sensor.

29. The non-transitory computer-readable medium of claim 28 , further comprising:

inputting the RF characteristic data to a trained model,

wherein determining that the RF source is present in the operating environment is based on an output of the trained model generated in response to receiving the RF characteristic data as input.

30. The non-transitory computer-readable medium of claim 25 , wherein the RF characteristic data indicates the RF signal.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 4, 2022
From: HARPE, BENJAMIN; STRUHL, ISAAC; WULFF, ALEXANDER
To: DISTRIBUTED SPECTRUM INC.
Reel/Frame 061308/0129 →
Continuity (3)
Provisional Application 63232605 · Aug 12, 2021
Provisional Application 63225130 · Jul 23, 2021
Related Publication 20230021777A1 · Jan 26, 2023
References Cited (72)
US 9485266B2 · Baxley et al. · 2016 [cited by applicant]
US 9485267B2 · Baxley et al. · 2016 [cited by applicant]
US 9551781B2 · Baxley et al. · 2017 [cited by applicant]
US 9560060B2 · Baxley et al. · 2017 [cited by applicant]
US 9591013B2 · Baxley et al. · 2017 [cited by applicant]
US 9635044B2 · Baxley et al. · 2017 [cited by applicant]
US 9736175B2 · Baxley et al. · 2017 [cited by applicant]
US 9739868B2 · Baxley et al. · 2017 [cited by applicant]
US 9880256B2 · Baxley et al. · 2018 [cited by applicant]
US 9945928B2 · Baxley et al. · 2018 [cited by applicant]
US 10104098B2 · Baxley et al. · 2018 [cited by applicant]
US 10122736B2 · Baxley et al. · 2018 [cited by applicant]
US 10338191B2 · Baxley et al. · 2019 [cited by applicant]
US 10473749B1 · Baxley et al. · 2019 [cited by applicant]
US 10578730B2 · Cardno et al. · 2020 [cited by applicant]
US 11259197B2 · Dzierwa et al. · 2022 [cited by applicant]
US 11916605B2 · Wulff et al. · 2024 [cited by applicant]
US 20130064328A1 · Adnani et al. · 2013 [cited by applicant]
US 20160127907A1 · Baxley et al. · 2016 [cited by applicant]
US 20160127931A1 · Baxley · 2016 [cited by examiner]
US 20170222719A1 · Haas · 2017 [cited by examiner]
US 20170238304A1 · Ling et al. · 2017 [cited by applicant]
US 20170261615A1 · Ying et al. · 2017 [cited by applicant]
US 20180324595A1 · Shima · 2018 [cited by applicant]
US 20200136711A1 · Cao · 2020 [cited by examiner]
US 20200142022A1 · Baxley et al. · 2020 [cited by applicant]
US 20200228834A1 · Li et al. · 2020 [cited by applicant]
US 20200252412A1 · Prasad Koppisetti et al. · 2020 [cited by applicant]
US 20210091829A1 · Shake et al. · 2021 [cited by applicant]
US 20220070822A1 · Behboodi et al. · 2022 [cited by applicant]
US 20220386249A1 · Meshkati et al. · 2022 [cited by applicant]
US 20220399946A1 · Marshall · 2022 [cited by applicant]
US 20230021777A1 · Wulff et al. · 2023 [cited by applicant]
US 20230027893A1 · Wulff et al. · 2023 [cited by applicant]
US 20230228834A1 · Gummadi · 2023 [cited by examiner]
US 20240248466A1 · Raman · 2024 [cited by applicant]
EP 3910822A1 · 2021 [cited by applicant]
U.S. Appl. No. 17/871,225, filed Jul. 22, 2022, Wulff et al. [cited by applicant]
U.S. Appl. No. 17/871,258, filed Jul. 22, 2022, Wulff et al. [cited by applicant]
PCT/US22/38012, Oct. 6, 2022, International Search Report and Written Opinion. [cited by applicant]
International Search Report and Written Opinion mailed Oct. 6, 2022, in connection with International Application No. PCT/US22/38012. [cited by applicant]
[No Author Listed], Fireside Hacks: A Conversation with Distributed Spectrum | NSIN Track. YouTube. Oct. 19, 2021. https://www.youtube.com/watch?v=CquBWk4Jq4E [Last Accessed Aug. 17, 2020]. 3 pages. [cited by applicant]
[No Author Listed], Modular , Real-Time RF Spectrum. Distributed Spectrum. 2021. 9 pages. [cited by applicant]
[No Author Listed], Modular and Highly-Scalable Spectrum. Distributed Spectrum, LLC. 2021. 1 page. [cited by applicant]
[No Author Listed], Report ITU-R SM.2355-1. SM Series Spectrum Management. 2019. 34 pages. [cited by applicant]
[No Author Listed], Radio Frequency Detection, Spectrum Analysis, and Direction Finding Equipment. Market Survey Report. NUSTL. Apr. 2019. 22 pages. [cited by applicant]
[No Author Listed], RTL-SDR Blog V3 Datasheet. https://www.rtl-sdr.com [Last Accessed Aug. 17, 2022] 8 pages. [cited by applicant]
[No Author Listed], TDOA Transmitter Localization with RTL-SDRs. Panoradio SDR. Jun. 8, 2022. 14 pages. [cited by applicant]
Aksu et al. Requirements for Spectrum Monitoring in Industrial Environments. NISTIR 8195. Nov. 2017. 24 pages. [cited by applicant]
Bhattacharya et al., Selection of Sensors for Efficient Transmitter Location. IEEE Infocom. 2020:2410-2419. [cited by applicant]
Cheng et al., An Algorithm for Jammer Localization in Wireless Sensor Networks. IEEE 26th International Conference on Advanced Information Networking and Applications. 2012:724-731. [cited by applicant]
Ezzati et. al. Distributed Spectrum Sensing Using Radio Environment Maps in Cognitive Radio Networks. Wireless Pers Communication. 2018;101:2241-2254. [cited by applicant]
Figueiredo et al., Low Cost SDR Solution for Spectrum Sensing. ResearchGate. Jul. 2015. 6 pages. [cited by applicant]
Hanna et al., Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity. arXiv:. Nov. 1, 20182; 1811.04521(1):7 pages. [cited by applicant]
Harp et al., Machine Vision and Deep Learning for Deep Learning for Classification of Radio SETI Signals. arXiv: Feb. 2019. 6;1902.02426: 31 pages. [cited by applicant]
Haykin et al., Cognitive Radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Comm. Feb. 2005;23(2):201-220. [cited by applicant]
Kumar et al., LTE Radio Analytics Made Easy and Accessible. SIGCOMM. Aug. 17-22, 2014. 12 pages. [cited by applicant]
Li et al., A Deep Convolutional Network for Multi-Type Signal Detection in Spectrogram. Preprints. May 12, 2020. 13 pages. [cited by applicant]
Li et al., Scaling Deep Learning Models for Spectrum Anomaly Detection. ACM.Jul. 2-5, 2019. 10 pages. [cited by applicant]
Morin et al., Deep Learning-based Transmitter identification on the physical layer. 2020. 23 pages. [cited by applicant]
Mosiane et al., Radio Frequency Interference Detection Using Machine Learning. IEEE Radio and Antenna Days of The Indian Ocean. IOP Conf. Series: Materials Science and Engineering. 2017;198(012012):5 pages. [cited by applicant]
Nika et al., Empirical Validation of Commodity Spectrum Monitoring. SenSys. Nov. 14-16, 2016. 12 pages. [cited by applicant]
O'Shea et al., Practical Signal Detection and Classification in GNU Radio. Proceedings of the SDR 07. Technical Conference and Product Exposition. 2007. 6 pages. [cited by applicant]
Pfammatter et al., A Software-defined Sensor Architecture for Large-scale Wideband Spectrum Monitoring. IPSN. Apr. 14-16, 2015. 12 pages. [cited by applicant]
Robertson et al., Distributed Transmitter Localization by Power Difference of Arrival (PDOA) on a Network of GNU Radio Sensors. Naval Research Laboratory. Feb. 2015. 3:16 pages. [cited by applicant]
Schmitz et al., Demonstration Abstract: Real-time Indoor Localization with TDOA and Distributed Software Defined Radio. IEEE. 2016. 2 pages. [cited by applicant]
Yiwere et al., Sound Source Distance Estimation Using Deep Learning: An Image Classifcaiton Approach. Sensors. 2020;20(172):19 pages. [cited by applicant]
Zha et al., A Deep Learning Framework for Signal Detection and Modulation Classification. Sensors. 2019; 19(4042): 21 pages. [cited by applicant]
Zubow et al., DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing. 2020 29th International Conference on Computer Communications and Networks (ICCCN). 2020:1-8. [cited by applicant]
International Preliminary Report on Patentability dated Feb. 1, 2024, in connection with International Application No. PCT/US2022/038012. [cited by applicant]
[No Author Listed], OmniSIG Sensor and the DeepWave AIR-T. DeepSig Blog. Apr. 13, 2021. 4 pages. https://www.deepsig.ai/news/omnisig-sensor-and-the-deepwave-air-t (Last accessed Aug. 7, 2023). [cited by applicant]
Hall et al., Reference Datasets for Training and Evaluating RF Signal Detection and Classification Models. IEEE. 2019. 5 Pages. [cited by applicant]