IP Library › Granted Patent US 12,618,983
Granted Patent B2
US 12,618,983 · App. 18/106,410 · Granted May 5, 2026

Methods and systems for excess path length corrections for GNSS receivers

Inventor: Lionel Garin (Palo Alto, CA)
Assignee: oneNav, Inc.
G01S19/41G01S19/396
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,618,983
App. No.
18/106,410
Granted
May 5, 2026
Kind
B2
Abstract

Methods and systems for estimating and using excess path length (EPL) corrections in GNSS receivers are described. A method can estimate the EPLs using a selection of line of sight and non line of sight pseudorange measurements, and these EPLs can be used to correct non selected non-line of sight pseudoranges. In one embodiment, a cloud based system can receive data from a crowd source set of EPL corrections (e.g., from GNSS receivers in an urban canyon environment) and then can develop a crowd sourced set of EPL corrections and then provide to GNSS receivers (some of which may part of the crowd of GNSS receivers) the crowd sourced set of EPL corrections. The EPL corrections can be used to improve position solutions in, for example, an urban canyon.

Claims (18)

1 . A method of operating a global navigation satellite system (GNSS) system, the method comprising:

receiving a set of estimated excess path lengths (EPLs) from one or more GNSS receivers, each of the estimated EPLs associated with an approximate location of the one or more GNSS receivers and a GNSS satellite's (SV's) position in a representation of sky above the approximate location at the approximate location at a time when pseudoranges were collected to determine the approximate location;

receiving the approximate location for each estimated EPL;

receiving or determining the GNSS SV's position in the representation of sky at the time and the approximate location;

selecting a bin based on the SV's position in the representation and the approximate location;

assigning one or more of the estimated EPLs to the selected bin;

determining, for the selected bin, an adjusted EPL value based on a set of estimated EPLs assigned to the selected bin.

2 . The method as in claim 1 , wherein the method is performed in a cloud based system that communicates with systems containing the one or more GNSS receivers and wherein the position of a GNSS SV is specified in a coordinate system that is one of: (a) azimuth and elevation; (b) a cartesian coordinate system; (c) a polar coordinate system; (d) a spherical coordinate system; or other coordinate systems that specify position relative to the approximate location.

3 . The method as in claim 2 , wherein the cloud based system further receives estimated EPL uncertainty for each EPL in the set of estimated EPLs.

4 . The method as in claim 2 , wherein the elevation and azimuth for each SV is determined from SV ephemeris data for each SV.

5 . The method as in claim 4 , wherein the assigning is based on a match between an estimated EPL's associated SV elevation and azimuth and a bin's elevation and azimuth and approximate location.

6 . The method as in claim 5 , wherein the method further comprises:

storing the adjusted EPL value in a database that is indexed by several precise grid locations and SV elevation and SV azimuth.

7 . The method as in claim 6 , wherein the adjusted EPL is computed as a weighted sum of the set of estimated EPLs assigned to the selected bin.

8 . The method as in claim 1 , wherein the method further comprises:

transmitting the adjusted EPL to another GNSS receiver for use in the another GNSS receiver to correct non-line of sight (NLOS) pseudoranges using the adjusted EPL.

9 . The method as in claim 1 , wherein the method further comprises:

transmitting the adjusted EPL to one of the one or more GNSS receivers for use in the one of the one or more GNSS receivers to correct non-line of sight (NLOS) pseudoranges using the adjusted EPL.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 15, 2023
From: GARIN, LIONEL
To: ONENAV, INC.
Reel/Frame 062704/0763 →
Continuity (3)
Provisional Application 63415245 · Oct 11, 2022
Provisional Application 63365710 · Jun 2, 2022
Related Publication 20230393287A1 · Dec 7, 2023
References Cited (35)
US 9562770B2 · Garin · 2017 [cited by applicant]
US 20090213006A1 · Nayyar et al. · 2009 [cited by applicant]
US 20160146945A1 · Kamijo et al. · 2016 [cited by applicant]
US 20170131409A1 · Irish et al. · 2017 [cited by applicant]
US 20200326430A1 · Nichols et al. · 2020 [cited by applicant]
US 20210239848A1 · Karvounis et al. · 2021 [cited by applicant]
US 20210325548A1 · Garin et al. · 2021 [cited by applicant]
US 20210373179A1 · McBurney et al. · 2021 [cited by applicant]
US 20220066041A1 · Khider · 2022 [cited by examiner]
US 20220066046A1 · Diggelen · 2022 [cited by examiner]
US 20220066048A1 · Diggelen et al. · 2022 [cited by applicant]
US 20220099841A1 · Farmer · 2022 [cited by examiner]
US 20220137236A1 · Conflitti et al. · 2022 [cited by applicant]
US 20230050047A1 · Maaref et al. · 2023 [cited by applicant]
WO 2020248200A1 · 2020 [cited by applicant]
WO 2021029869A1 · 2021 [cited by applicant]
Lyu, Zhitao et al. A New Method for Non-line-of-sight GNSS Signal Detection for Positioning Accuracy Improvement in Urban Environments. In Proceedings of the 33rd International Technical Meeting of the Satellite Divisio… [cited by applicant]
Suzuki, T. et al. NLOS multipath detection using convolutional neural network. In Proceedings of the 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation, (ION GNSS+ 2020), Sep. … [cited by applicant]
Teunissen, Peter and Montenbruck, Oliver, editors. “Combination of Observations”, Chapter 20, Springer Handbook of Global Navigation Satellite Systems (1st. ed.). Springer Publishing Company, Incorporated, 2017, pp. 583… [cited by applicant]
U.S. Appl. No. 63/362,931, filed Apr. 13, 2022, 40 pages. [cited by applicant]
PCT International Search Report and Written Opinion from related PCT Application No. PCT/US2023/023889, mailed Oct. 23, 2023 (15 pages). [cited by applicant]
A. A. Abdallah and Z. M. Kassas, “Deep Learning-Aided Spatial Discrimination for Multipath Mitigation,” 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) (Apr. 20-23, 2020), pp. 1324-1335, doi: 10.1109/P… [cited by applicant]
F. Dovis, R. Imam, W. Qin, C. Savas and H. Visser, “Opportunistic use of GNSS Signals to Characterize the Environment by Means of Machine Learning Based Processing,” ICASSP 2020—2020 IEEE International Conference on Aco… [cited by applicant]
B. Guermah, H. E. Ghazi, T. Sadiki and H. Guermah, “A Robust GNSS LOS/Multipath Signal Classifier based on the Fusion of Information and Machine Learning for Intelligent Transportation Systems,” 2018 IEEE International … [cited by applicant]
L.T. Hsu, “GNSS multipath detection using a machine learning approach,” 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (Oct. 2017), pp. 1-6, doi: 10.1109/ITSC.2017.8317700. [cited by applicant]
Munin, Evgenii & Blais, Antoine & Couellan, Nicolas. Convolutional Neural Network for Multipath Detection in GNSS Receivers. arXiv:1911.02347v1 [eess.SP] (Nov. 6, 2019), pp. 1-10, https://doi.org/10.48550/arXiv.1911.023… [cited by applicant]
M. Orabi, J. Khalife, A. A. Abdallah, Z. M. Kassas and S. S. Saab, “A Machine Learning Approach for GPS Code Phase Estimation in Multipath Environments,” 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)… [cited by applicant]
Qin, Honglei & Xue, Xia & Yang, Qian. GNSS multipath estimation and mitigation based on particle filter. IET Radar, Sonar & Navigation, vol. 13, Issue 9 (Sep. 1, 2019), pp. 1588-1596. https://doi.org/10.1049/iet-rsn.201… [cited by applicant]
Quan Y, Lau L, Roberts GW, Meng X, Zhang C. Convolutional Neural Network Based Multipath Detection Method for Static and Kinematic GPS High Precision Positioning. Remote Sensing. (Dec. 17, 2018), 10(12):2052. https://do… [cited by applicant]
Savas, Caner, Dovis, Fabio, “Multipath Detection based on K-means Clustering,” Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Fl… [cited by applicant]
Suzuki, Taro, Nakano, Yusuke, Amano, Yoshiharu, “NLOS Multipath Detection by Using Machine Learning in Urban Environments,” Proceedings of the 30th International Technical Meeting of the Satellite Division of The Instit… [cited by applicant]
Suzuki T, Amano Y. NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning. Sensors. (Apr. 3, 2021), 21(7):2503. https://doi.org/10.3390/s21072503. [cited by applicant]
Yozevitch, R., Moshe, B. B., and Weissman, A. A Robust GNSS LOS/NLOS Signal Classifier. J Inst Navig, 63:429-442. (May 2016) doi: 10.1002/navi.166. [cited by applicant]
Hoque, Mainul and Jakowski, N. “Estimate of higher order ionospheric errors in GNSS positioning”, Radio Science, vol. 43, RS5008, doi:10.1029/2007RS003817, Oct. 9, 2008 (15 pages). [cited by applicant]
PCT International Search Report and Written Opinion from related PCT Application No. PCT/US2022/33617, mailed Sep. 12, 2022 (22 pages). [cited by applicant]