IP Library › Granted Patent US 12,382,247
Granted Patent B2
US 12,382,247 · App. 17/876,264 · Granted Aug 5, 2025

Vehicle road side location of a target via unwrapped differential phase RF signals

Inventors: Sirajum Munir (Pittsburgh, PA); Hongkai Chen (Smithtown, NY)
Assignee: Robert Bosch GmbH
H04W4/025G06N20/00H04B7/0626H04B7/0682
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 12,382,247
App. No.
17/876,264
Granted
Aug 5, 2025
Kind
B2
Abstract

A vehicle side target location method includes receiving a request, via a first wireless connection, for the vehicle to travel to a location, in response to the vehicle being less than a predetermined distance from the location, receiving RF packets, via a second wireless connection having a multiple antenna radio frequency (RF) transceiver having an identification (ID), from a target at the location, identifying packets based on the ID of the RF transceiver, extracting channel state information (CSI) from received signals associated with the identified packets, determining a phase difference of subcarrier phase data of the received signals between each of the multiple antennae, filtering noise of the phase difference of subcarriers based on subcarrier selection to obtain multiple robust phase difference signals, and feeding the multiple robust phase difference signals to a classifier to obtain a side of the vehicle associated with the location of the target.

Claims (49)

1. A system for a vehicle comprising:

a multiple antenna radio frequency (RF) transceiver having an identification (ID); and

a processor coupled with the RF transceiver and configured to:

receive a request, via a first wireless connection, for the vehicle to travel to a location,

in response to the vehicle being less than a predetermined distance from the location, receive RF packets, via a second wireless connection, from a target at the location,

identify packets based on the ID of the RF transceiver,

extract channel state information (CSI) from received signals associated with the identified packets,

unwrap phase from the CSI of the received signals to obtain subcarrier phase data,

determine a phase difference of subcarriers of the received signals between each of the multiple antennae,

filter noise of the phase difference based on subcarrier selection to obtain multiple robust phase difference signals, and

feed the multiple robust phase difference signals to a Long Short-Term Memory (LSTM) classifier to obtain a side of the vehicle associated with the location of the target.

2. The system of claim 1 , wherein the subcarrier selection is all received subcarriers.

3. The system of claim 1 , wherein the subcarrier selection is based on a variance of phase differences of received subcarriers to obtain selected subcarriers, and wherein the multiple robust phase difference signals is an average phase difference of the selected subcarriers.

4. The system of claim 1 , wherein the multiple antenna RF transceiver in configured to support at least one of 802.11 (Wi-Fi), Ultra-Wide Band (UWB), and Bluetooth (BT).

5. The system of claim 1 , wherein the multiple antenna RF transceiver includes three antennae.

6. The system of claim 1 , wherein the classifier is an LSTM, SVM, or decision tree classifier.

7. The system of claim 1 , wherein the target is configured as an access point and the RF transceiver is configured as a client to the target.

8. The system of claim 1 , wherein the RF transceiver is configured as an access point (Hot spot) and the target is configured as a client to the RF transceiver.

9. The system of claim 8 , wherein the RF transceiver selects a channel and band based on RF traffic to minimize interference and transmits the channel and band to the target via the first wireless connection.

10. The system of claim 1 , wherein the RF transceiver is configured to monitor an ID, channel, and band associated with the target.

11. A vehicle side target location method comprising:

receiving a request, via a first wireless connection, for the vehicle to travel to a location;

in response to the vehicle being less than a predetermined distance from the location, receiving RF packets, via a second wireless connection having a multiple antenna radio frequency (RF) transceiver having an identification (ID), from a target at the location;

identifying packets based on the ID of the RF transceiver;

extracting channel state information (CSI) from received signals associated with the identified packets;

unwrapping phase from the CSI of the received signals to obtain subcarrier phase data;

determining a phase difference of subcarrier phase data of the received signals between each of the multiple antennae;

filtering noise of the phase difference of subcarriers based on subcarrier selection to obtain multiple robust phase difference signals; and

feeding the multiple robust phase difference signals to a Long Short-Term Memory (LSTM) classifier to obtain a side of the vehicle associated with the location of the target.

12. The method of claim 11 , wherein the subcarrier selection is all received subcarriers.

13. The method of claim 11 , wherein the subcarrier selection is based on a variance of phase differences of received subcarriers to obtain selected subcarriers, and wherein the multiple robust phase difference signals is an average phase difference of the selected subcarriers.

14. The method of claim 11 , wherein the multi antenna RF transceiver in configured to support at least one of 802.11 (Wi-Fi), Ultra-Wide Band (UWB), and Bluetooth (BT).

15. A system for performing lane selection associated with autonomous control of a vehicle, the system comprising:

a multiple antenna radio frequency (RF) transceiver having an identification (ID);

a processor coupled with the RF transceiver; and

a memory including instructions that, when executed by the processor, cause the processor to:

receive a request, via a first wireless connection, for the vehicle to travel to a location,

in response to the vehicle being less than a predetermined distance from the location, receive RF packets, via a second wireless connection, from a target at the location,

identify packets based on the ID of the RF transceiver,

extract channel state information (CSI) from received signals associated with the identified packets,

determine a phase difference of subcarriers of the received signals between each of the multiple antennae,

filter noise of the phase difference of subcarriers based on subcarrier selection to obtain multiple robust phase difference signals,

feed the multiple robust phase difference signals to a Long Short-Term Memory (LSTM) classifier to obtain a side of the vehicle associated with the location of the target, and

operate the vehicle to navigate the vehicle to a lane associated with the side of the vehicle.

16. The system of claim 15 , wherein the memory includes further instructions that, when executed by the processor, cause the processor to perform phase unwrapping after extraction of the CSI.

17. The system of claim 15 , wherein the subcarrier selection is all received subcarriers.

18. The system of claim 15 , wherein the subcarrier selection is based on a variance of phase differences of received subcarriers to obtain selected subcarriers, and wherein the multiple robust phase difference signals is an average phase difference of the selected subcarriers.

19. The system of claim 15 , wherein the multi antenna RF transceiver in configured to support at least one of 802.11 (Wi-Fi), Ultra-Wide Band (UWB), and Bluetooth (BT).

20. The system of claim 15 , further comprising 3 antennae coupled with the RF transceiver.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 1, 2022
From: MUNIR, SIRAJUM; CHEN, HONGKAI
To: ROBERT BOSCH GMBH
Reel/Frame 060685/0414 →
Continuity (1)
Related Publication 20240040332A1 · Feb 1, 2024
References Cited (60)
US 10382942B2 · Coulis et al. · 2019 [cited by applicant]
US 20120149414A1 · Krishnaswamy et al. · 2012 [cited by applicant]
US 20130094484A1 · Kneckt et al. · 2013 [cited by applicant]
US 20160062778A1 · Hill · 2016 [cited by applicant]
US 20170086204A1 · Jung et al. · 2017 [cited by applicant]
US 20170256034A1 · Bai et al. · 2017 [cited by applicant]
US 20170339739A1 · Pallen et al. · 2017 [cited by applicant]
US 20180096545A1 · Spahl et al. · 2018 [cited by applicant]
US 20180341274A1 · Donnelly · 2018 [cited by examiner]
US 20180365975A1 · Xu et al. · 2018 [cited by applicant]
US 20190128683A1 · Kristinsson et al. · 2019 [cited by applicant]
US 20200296779A1 · Moghe et al. · 2020 [cited by applicant]
US 20200314590A1 · Odejerte, Jr. · 2020 [cited by applicant]
US 20200359215A1 · Akpinar et al. · 2020 [cited by applicant]
US 20200383153A1 · Agarwal et al. · 2020 [cited by applicant]
US 20210405173A1 · Elias · 2021 [cited by examiner]
US 20220024476A1 · Lund et al. · 2022 [cited by applicant]
US 20220135085A1 · Wright et al. · 2022 [cited by applicant]
US 20220322085A1 · De Vegt et al. · 2022 [cited by applicant]
US 20230209469A1 · Foster et al. · 2023 [cited by applicant]
US 20230296785A1 · Sharma et al. · 2023 [cited by applicant]
US 20230300690A1 · Tabet et al. · 2023 [cited by applicant]
US 20230421207A1 · Ellenbeck et al. · 2023 [cited by applicant]
US 20240007148A1 · Ellenbeck et al. · 2024 [cited by applicant]
CN 110888110A · 2020 [cited by applicant]
Wesbite https://medium.muz.li/uber-compass-an-ar-ux-ui-case-study-569dd2e9f650 Nikhil Muzli, “Uber Compass, an AR UX/UI Case Study,” dated Jul. 16, 2020, Retrieved Jul. 28, 2022, 35 Pages. [cited by applicant]
Wesbite https://www.air802.com/sector-antenna-wifi-2.4-ghz-12-dbi-180-degree-vertical-polarized.html “Sector Antenna, 2.4 GHz WiFi, 12dBi, 180 degree,” Retrieved Jul. 28, 2022, 3 Pages. [cited by applicant]
Website https://www.signalbooster.com/products/wifi-5-ghz-and-2-4-ghz-directional-10-dbi-panel-antenna “WiFi Antenna: 5 GHZ and 2.4 GHz Directional 10 DBi Panel Antenna,” Retreived Jul. 28, 2022, 7 Pages. [cited by applicant]
Shiwei Fang et al., “EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching,” Int. Conf. Distrib. Comput. Sens. Syst. Workshops, May 2020, pp. 59-68. [cited by applicant]
Daniel Halperin et al., “Tool Release: Gathering 802.11n Traces with Channel State Information,” ACM SIGCOMM Computer Communication Review Jan. 2011, vol. 41, No. 1, pp. 53-53. [cited by applicant]
Wenjun Jiang et al., “Towards 3D Human Pose Construction Using WiFi,” Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, Sep. 21-25, 2020, pp. 1-14. [cited by applicant]
Manikanta Kotaru et al., “SpotFi: Decimeter Level Localization Using WiFi,” ACM SIGCOMM computer communication review, 2015, vol. 45, pp. 269-282. [cited by applicant]
Hanchuan Li et al., “ID-Match: A Hybrid Computer Vision and RFID System for Recognizing Individuals in Groups,” Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, ACM, 2016, pp. 4933-4944. [cited by applicant]
Liu Liu et al., “Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map,” Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2372-2381. [cited by applicant]
Yongsen Ma et al., “WiFi Sensing with Channel State Information: A Survey,” ACMComput. Surv., vol. 52, No. 3, Article 46, Publication date: Jun. 2019, 36 Pages. [cited by applicant]
Yongsen Ma et al., “SignFi: Sign Language Recognition Using WiFi,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, No. 1, Article 23. Publication date: Mar. 2018, 21 Pages. [cited by applicant]
Sirajum Munir et al., “Real-Time Fine Grained Occupancy Estimation using Depth Sensors on ARM Embedded Platforms,” 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 12 Pages. [cited by applicant]
Dinh-Van Nguyen et al., “WiFi Fingerprinting Localization for Intelligent Vehicles in Car Park”, 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 24-27. [cited by applicant]
Alejandro Blanco Pizarro et al., “Accurate Ubiquitous Localization with Off-the-Shelf IEEE 802.11ac Devices,” Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, 2021, … [cited by applicant]
Inioluwa Deborah Raji et al., “About Face: A Survey of Facial Recognition Evaluation,” arXiv preprint arXiv:2102.00813, 2021, 11 Pages. [cited by applicant]
Ralph Schmidt, “Multiple Emitter Location and Signal Parameter—Estimation,” IEEE Transactions on Antennas and Propagation, Mar. 1986, vol. AP-34, No. 3, pp. 276-280. [cited by applicant]
Elahe Soltanaghaei et al., “Multipath Triangulation: Decimeter-level WiFi Localization and Orientation with a Single Unaided Receiver,” The 16th Annual International Conference on Mobile Systems, Applications, and Servi… [cited by applicant]
Deepak Vasisht et al., “Decimeter-Level Localization with a Single WiFi Access Point,” 13th USENIX Symposium on Networked Systems Design and Implementation 2016 (NSDI '16), pp. 165-178. [cited by applicant]
Chen Wang et al., “Robust Chinese Remainder Theorem Ranging Method Based on Dual-Frequency Measurements,” IEEE Transactions on Vehicular Technology, Oct. 2011, vol. 60, No. 8, pp. 4094-4099. [cited by applicant]
He Wang et al., “InSight: Recognizing Humans without Face Recognition”, ACM HotMobile'13, Feb. 26-27, 2013, 6 Pages. [cited by applicant]
Hongsong Wang et al., “Learning Content and Style: Joint Action Recognition and Person Identi cation from Human Skeletons,” Preprint submitted to Journal of LATEX Templates Mar. 20, 2018, 35 Pages. [cited by applicant]
Ju Wang et al., “LiFS: Low Human-Effort, Device-Free Localization with Fine-Grained Subcarrier Information,” MobiCom '16: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking: New Y… [cited by applicant]
Genta Indra Winata et al., “Onthe Effectiveness of Low-Rank Matrix Factorization for LSTMModelCompression,” arXiv:1908.09982v1 [cs.CL] Aug. 27, 2019, 10 Pages. [cited by applicant]
Dan Wu et al., “WiDir: Walking Direction Estimation Using Wireless Signals,” UBICOMP '16, Sep. 12-16, 2016, Heidelberg, Germany, pp. 351-362. [cited by applicant]
Yaxiong Xie et al., “Precise Power Delay Profiling with Commodity Wi-Fi,” IEEE Transactions on Mobile Computing, 2018, vol. 18, No. 6, pp. 1342-1355. [cited by applicant]
Jie Xiong et al., “ArrayTrack: A Fine-Grained Indoor Location System,” 10th USENIX Symposium on Networked Systems Design and Implementation 2013 (NSDI '13), pp. 71-84. [cited by applicant]
Hua Xue et al., “WiZoom: Accurate Multipath Profiling using Commodity WiFi Devices with Limited Bandwidth,” 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), IEEE, 2019, 1… [cited by applicant]
Rui Zhang et al., Wifi sensing-based real-time bus tracking and arrival time prediction in urban environments. IEEE Sensors Journal, 2018, vol. 18, No. 11, pp. 4746-4760. [cited by applicant]
Michael H. Zhu et al., “To prune, or not to prune: exploring the efficacy of pruning for model compression,” arXiv:1710.01878v2 [stat.ML] Nov. 13, 2017, 11 Pages. [cited by applicant]
Nguyen Dinh-Van et al., “Indoor Intelligent Vehicle localization using WiFi received signal strength indicator,” IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), 2017, pp. 33-36. [cited by applicant]
Mohamed Ibrahim et al., “Wi-Go: accurate and scalable vehicle positioning using WiFi fine timing measurement,” Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, Jun. 2020, p… [cited by applicant]
David Fernandez Llorca et al., “Recognizing individuals in groups in outdoor environments combining stereo vision, rfid and ble,” Cluster Computing, 2017, vol. 20, No. 1, pp. 769-779. [cited by applicant]
Dapeng Tao et al., “Deep Multi-View Feature Learning for Person Re-Identification,” IEEE Transactions on Circuits and Systems for Video Technology, Oct. 2018, vol. 28, No. 10, pp. 2657-2666. [cited by applicant]
Mwei Jin et al., “3D reconstruction using deep learning: a survey,” Communications in Information and Systems, 2020, vol. 20, No. 4, pp. 389-413. [cited by applicant]
Book David Tse et al., “Fundamentals of wireless communication,” Dec. 9, 2004, Cambridge university press, 2005, 644 Pages. [cited by applicant]