IP Library Granted Patent US 12,430,406
Granted Patent B2
US 12,430,406 · App. 19/024,493 · Granted Sep 30, 2025

Systems and methods of sensor data fusion

Inventor: Armando Montalvo (Winter Garden, FL)
Assignee: Digital Global Systems, Inc.
G06F18/256B60R19/483B60T8/1755B60T8/3275B60W10/18B60W10/184B60W30/085B60W30/09B60W60/0018B60W60/00186B62D15/0285G01C3/00G01C21/3804G01C21/3811G01C21/3848G01C22/00G01S5/14G01S11/00G01S13/08G01S13/103G01S13/42G01S15/08G01S15/101G01S15/42G01S17/08G01S17/88G01S17/894G05B13/0205G06F7/14G06F7/16G06F16/24G06F16/245G06F16/2455G06F16/24556G06F16/2456G06F16/33G06F16/334G06F16/43G06F16/53G06F16/903G06F16/90335G06F16/9035G06F17/18G06F18/217G06F18/2431G06F18/25G06F18/251G06N3/02G06N3/0464G06N5/022G06N5/042G06N5/045G06N5/046G06N5/048G06T7/521G06V10/764G06V10/803G06V10/82G06V20/56H04L67/12H04W4/38B25J9/1664B25J9/1694B60T2201/00B60T2201/03B60W2050/0052B60W60/00B60W2420/00B60W2420/40B60W2420/403B60W2420/408B60W2420/50B60W2510/069B60W2510/18B60W2520/04B60W2540/12B60W2554/801B60W2554/802B60W2556/35B60W2710/18B60W2754/30G01C21/1652G01S7/4808G01S2013/93185G05D2101/15G05D2111/50G05D2111/67G06F18/213G06N5/04G06N20/00G06T2207/10028G06T2207/20024G06T2207/20084G06T2207/30252G06T2207/30264G06V10/80G08B29/188G08G1/0133G08G1/04G08G1/042
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 12,430,406
App. No.
19/024,493
Granted
Sep 30, 2025
Kind
B2
Abstract

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Claims (68)

1. A system for sensor data fusion for sensor management and utilization in autonomous transportation, comprising:

at least one computer processor including a memory;

at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine;

at least one first distance sensor operable to capture a first distance measurement from a vehicle to at least one object; and

at least one second distance sensor operable to capture a second distance measurement from the vehicle to the at least one object;

wherein the at least one computer processor is operable to analyze the first distance measurement and the second distance measurement;

wherein the at least one computer processor is operable to receive at least one query;

wherein the at least one curation engine is operable to curate the first distance measurement and the second distance measurement, the at least one link engine is operable to link the first distance measurement and the second distance measurement, the at least one fusion engine is operable to fuse the first distance measurement and the second distance measurement, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first distance measurement and the second distance measurement, and the at least one validation engine is operable to validate the first distance measurement and the second distance measurement;

wherein the fused data includes at least one new data set;

wherein the at least one validation engine is operable to actively validate the fused data;

wherein the active validation of the fused data includes modification of an orientation of the at least one first distance sensor, and the at least one first distance sensor capturing a third distance measurement from the vehicle to the at least one object at the modified orientation to generate a second data set;

wherein the active validation of the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison;

wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data;

wherein the at least one new data set includes an accuracy value for the at least one first distance sensor and the at least one second distance sensor;

wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set;

wherein the at least one inference engine is operable to answer the at least one query based in part on the at least one inference; and

wherein the at least one computer processor is operable to instruct the vehicle to brake based on the at least one new data set.

2. The system of claim 1 , wherein the answer to the at least one query includes a distance from the vehicle to the at least one object.

3. The system of claim 1 , wherein the at least one inference engine is operable to determine the at least one inference in real-time.

4. The system of claim 1 , wherein the at least one inference engine is operable to determine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.

5. The system of claim 1 , wherein the at least one inference includes a numerical value.

6. The system of claim 1 , wherein the at least one inference includes a prediction of a future event based in part on the fused data.

7. A method for sensor data fusion for sensor management and utilization in autonomous transportation, comprising:

providing at least one computer processor including a memory;

providing at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine;

at least one first distance sensor capturing a first distance measurement from a vehicle to at least one object;

at least one second distance sensor capturing a second distance measurement from the vehicle to the at least one object;

analyzing by the at least one computer processor the first distance measurement and the second distance measurement;

receiving by the at least one computer processor at least one query;

curating by the at least one curation engine the first distance measurement and the second distance measurement, linking by the at least one link engine the first distance measurement and the second distance measurement, fusing by the at least one fusion engine the first distance measurement and the second distance measurement, thereby creating fused data, determining at least one inference by the at least one inference engine from the first distance measurement and the second distance measurement, and validating by the at least one validation engine the first distance measurement and the second distance measurement;

wherein the fused data includes at least one new data set;

actively validating by the at least one validation engine the fused data;

wherein actively validating the fused data includes modifying an orientation of the at least one first distance sensor, and the at least one first distance sensor capturing a third distance measurement from the vehicle to the at least one object at the modified orientation, thereby generating a second data set;

wherein actively validating the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison;

wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data;

wherein the at least one new data set includes an accuracy value for the at least one first distance sensor and the at least one second distance sensor;

determining via the at least one inference engine the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set;

answering via the at least one inference engine the at least one query based in part on the at least one inference; and

instructing by the at least one computer processor movement of the vehicle based on the at least one new data set.

8. The method of claim 7 , wherein answering via the at least one inference engine the at least one query includes a distance from the vehicle to the at least one object.

9. The method of claim 7 , wherein the at least one inference includes a numerical value.

10. The method of claim 7 , further comprising predicting via the at least one inference engine a future event based in part on the fused data.

11. The method of claim 7 , wherein determining the at least one inference occurs in real-time.

12. The method of claim 7 , further comprising determining via the at least one inference engine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.

13. The method of claim 7 , further comprising validating the at least one inference by the at least one validation engine by comparing the at least one inference to a second inference.

14. A system for sensor data fusion for sensor management and utilization in autonomous transportation, comprising:

at least one computer processor including a memory;

at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; and

at least two sensors, each of the at least two sensors operable to measure a first distance from a vehicle to at least one object and a second distance from the vehicle to the at least one object;

wherein the at least one computer processor is operable to analyze the first distance and the second distance;

wherein the at least one computer processor is operable to receive at least one query;

wherein the at least one curation engine is operable to curate the first distance and the second distance, the at least one link engine is operable to link the first distance and the second distance, the at least one fusion engine is operable to fuse the first distance and the second distance, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first distance and the second distance, and the at least one validation engine is operable to validate the first distance and the second distance;

wherein the fused data includes at least one new data set;

wherein the at least one validation engine is operable to actively validate the fused data;

wherein the active validation of the fused data includes modification of an orientation of at least one of the at least two sensors, the at least one of the at least two sensors capturing a third distance measurement from the vehicle to the at least one object at the modified orientation to generate a second data set;

wherein the active validation of the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison;

wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data;

wherein the at least one new data set includes an accuracy value for each of the at least two sensors;

wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set;

wherein the at least one inference engine is operable to answer the at least one query based in part on the at least one inference;

wherein the at least one validation engine is operable to validate the at least one inference by comparing the at least one inference to a second inference; and

wherein the at least one computer processor is operable to instruct the vehicle to brake based on the at least one new data set.

15. The system of claim 14 , wherein the answer to the at least one query includes a distance from the vehicle to the at least one object.

16. The system of claim 14 , wherein the at least one inference engine is operable to determine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.

17. The system of claim 14 , wherein the at least one inference includes a numerical value.

18. The system of claim 14 , wherein the at least one inference engine is operable to determine the at least one inference in real-time.

19. The system of claim 14 , wherein the at least one inference includes a prediction of a future event based in part on the fused data.

20. The system of claim 14 , wherein the at least one new data set includes an accuracy value for each of the at least two sensors.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 13, 2025
From: MONTALVO, ARMANDO
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 070214/0056 →
Continuity (3)
Continuation 18990161 · Dec 20, 2024
Continuation 18988120 · Dec 19, 2024
Related Publication 20250156504A1 · May 15, 2025
References Cited (113)
US 6026340A · Corrado et al. · 2000 [cited by applicant]
US 9367683B2 · Kolacinski et al. · 2016 [cited by applicant]
US 9383753B1 · Templeton · 2016 [cited by examiner]
US 10073456B2 · Mudalige · 2018 [cited by examiner]
US 10095230B1 · Hardin · 2018 [cited by applicant]
US 10748075B2 · Chowdhary et al. · 2020 [cited by applicant]
US 10766137B1 · Porter et al. · 2020 [cited by applicant]
US 11037320B1 · Ebrahimi Afrouzi et al. · 2021 [cited by applicant]
US 11040444B2 · Cristache · 2021 [cited by applicant]
US 11436429B2 · Jaganathan et al. · 2022 [cited by applicant]
US 11586865B2 · Brahma et al. · 2023 [cited by applicant]
US 11966413B2 · Latapie et al. · 2024 [cited by applicant]
US 11983625B2 · Ahuja et al. · 2024 [cited by applicant]
US 12105211B2 · Carcanague et al. · 2024 [cited by applicant]
US 12112174B2 · Sodani et al. · 2024 [cited by applicant]
US 12174301B2 · Hu · 2024 [cited by examiner]
US 12217831B2 · Jaganathan et al. · 2025 [cited by applicant]
US 12287851B1 · Montalvo · 2025 [cited by applicant]
US 20040019575A1 · Talbot et al. · 2004 [cited by applicant]
US 20070239314A1 · Kuvich · 2007 [cited by applicant]
US 20080005075A1 · Horvitz et al. · 2008 [cited by applicant]
US 20080312756A1 · Grichnik et al. · 2008 [cited by applicant]
US 20120134280A1 · Rotvold et al. · 2012 [cited by applicant]
US 20150306770A1 · Mittal et al. · 2015 [cited by applicant]
US 20170004411A1 · Hassan et al. · 2017 [cited by applicant]
US 20170048010A1 · Chowdhery et al. · 2017 [cited by applicant]
US 20180284758A1 · Cella et al. · 2018 [cited by applicant]
US 20190206400A1 · Cui et al. · 2019 [cited by applicant]
US 20190258251A1 · Ditty · 2019 [cited by examiner]
US 20190295001A1 · Fusco et al. · 2019 [cited by applicant]
US 20200026289A1 · Alvarez et al. · 2020 [cited by applicant]
US 20200116837A1 · Aghari · 2020 [cited by examiner]
US 20200219316A1 · Baik et al. · 2020 [cited by applicant]
US 20200303033A1 · Benz et al. · 2020 [cited by applicant]
US 20200405403A1 · Shelton, IV et al. · 2020 [cited by applicant]
US 20210150230A1 · Smolyanskiy et al. · 2021 [cited by applicant]
US 20210278523A1 · Urtasun · 2021 [cited by examiner]
US 20210290311A1 · Fuerst et al. · 2021 [cited by applicant]
US 20210318121A1 · Laroche et al. · 2021 [cited by applicant]
US 20210331695A1 · Ramakrishnan · 2021 [cited by examiner]
US 20210342656A1 · Mittal et al. · 2021 [cited by applicant]
US 20220012562A1 · Lazaro-Gredilla et al. · 2022 [cited by applicant]
US 20220035376A1 · Laddah · 2022 [cited by examiner]
US 20220075077A1 · Kato et al. · 2022 [cited by applicant]
US 20220126864A1 · Moustafa · 2022 [cited by examiner]
US 20220146652A1 · Cardei et al. · 2022 [cited by applicant]
US 20220161815A1 · Van Beek et al. · 2022 [cited by applicant]
US 20220179056A1 · Braley · 2022 [cited by examiner]
US 20220289175A1 · Kasarla et al. · 2022 [cited by applicant]
US 20220324464A1 · Collin et al. · 2022 [cited by applicant]
US 20220348223A1 · Zhao · 2022 [cited by examiner]
US 20230038842A1 · Yu · 2023 [cited by examiner]
US 20230082656A1 · Li et al. · 2023 [cited by applicant]
US 20230110027A1 · Bajpayee et al. · 2023 [cited by applicant]
US 20230112441A1 · Tang · 2023 [cited by examiner]
US 20230149101A1 · Mottram et al. · 2023 [cited by applicant]
US 20230166765A1 · Yoon · 2023 [cited by examiner]
US 20230182768A1 · Oh · 2023 [cited by examiner]
US 20230190204A1 · Helm et al. · 2023 [cited by applicant]
US 20230256618A1 · Park et al. · 2023 [cited by applicant]
US 20230270506A1 · Goodchild et al. · 2023 [cited by applicant]
US 20230342944A1 · Sullivan et al. · 2023 [cited by applicant]
US 20230347517A1 · Ma et al. · 2023 [cited by applicant]
US 20230394334A1 · Kang et al. · 2023 [cited by applicant]
US 20240045426A1 · Ditty et al. · 2024 [cited by applicant]
US 20240058948A1 · Zhai et al. · 2024 [cited by applicant]
US 20240112428A1 · Levi et al. · 2024 [cited by applicant]
US 20240123619A1 · Ning · 2024 [cited by applicant]
US 20240138926A1 · Goodchild et al. · 2024 [cited by applicant]
US 20240142994A1 · Ebrahimi Afrouzi · 2024 [cited by applicant]
US 20240144082A1 · Tarapov et al. · 2024 [cited by applicant]
US 20240152734A1 · Ye · 2024 [cited by applicant]
US 20240159891A1 · Patel · 2024 [cited by applicant]
US 20240249165A1 · Roche et al. · 2024 [cited by applicant]
US 20240267779A1 · Wu · 2024 [cited by examiner]
US 20240289930A1 · Yang et al. · 2024 [cited by applicant]
US 20240289981A1 · Kuo et al. · 2024 [cited by applicant]
US 20240296044A1 · Day · 2024 [cited by examiner]
US 20240312219A1 · Choi et al. · 2024 [cited by applicant]
US 20240317254A1 · Tran · 2024 [cited by examiner]
US 20240324838A1 · Ebrahimi Afrouzi · 2024 [cited by examiner]
US 20240331403A1 · Shen et al. · 2024 [cited by applicant]
US 20240378412A1 · Schrader · 2024 [cited by applicant]
US 20240382216A1 · Hage et al. · 2024 [cited by applicant]
US 20240408495A1 · Hibi et al. · 2024 [cited by applicant]
US 20240412494A1 · Balachandran et al. · 2024 [cited by applicant]
US 20250026371A1 · Andert et al. · 2025 [cited by applicant]
US 20250053731A1 · Kantimahanti et al. · 2025 [cited by applicant]
US 20250130316A1 · Asghari · 2025 [cited by examiner]
US 20250139807A1 · Zou et al. · 2025 [cited by applicant]
US 20250200431A1 · Marzban et al. · 2025 [cited by applicant]
CN 110598299A · 2019 [cited by applicant]
CN 110909762A · 2020 [cited by applicant]
CN 115561795A · 2023 [cited by applicant]
CN 116522279A · 2023 [cited by applicant]
CN 118427205A · 2024 [cited by applicant]
WO 2024186551A1 · 2024 [cited by applicant]
WO 2024228863A1 · 2024 [cited by applicant]
Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140. (Year: 2021). [cited by applicant]
Lanegger et al., “To Fuse or Not to Fuse: Measuring Consistency in Multi-Sensor Fusion for Aerial Robots” Springer, Aug. 2024 (Year: 2024). [cited by applicant]
Liu et al., “A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion” Sensors 2015, 15, 7933-7952 (Year: 2015). [cited by applicant]
Chen et al., “Track correlation uncertainty evaluation for Multi-sensor Data Fusion System and its application” 2021 IEEE (Year: 2021). [cited by applicant]
Peli et al., “Feature Level Sensor Fusion” Part of the SPIE Conference on Sensor Fusion: Architectures. Algorithms, and Applications III. Orlando, Florida, Apr. 1999 (Year: 1999). [cited by applicant]
Brush, “Measurement of microwave power—A review of techniques used for measurement of high-frequency RF power” IEEE Instrumentation & Measurement Magazine Apr. 2007 (Year: 2007). [cited by applicant]
Kleber et al., “Cooperative Cross-Correlation Algorithm to Optimize Linearity of Fused RF Sensors” IEEE Sensors Journal, vol. 20, No. 7, Apr. 1, 2020 (Year: 2020). [cited by applicant]
Ebel, Patrick, and et al. “SEN12MS-CR-TS: A remote-sensing data set for multi modal multitemporal cloud removal.” IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-14 (Year: 2022). [cited by applicant]
Wei Z, and et al. MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review. Sensors (Basel). Mar. 25, 2022;22(7):2542. doi: 10.3390/s22072542. PMID: 35408157; PMCID: PMC9003130 (Year: 2022). [cited by applicant]
CN110598299—English translation. [cited by applicant]
CN115561795—English translation. [cited by applicant]
Rumfelt et al., “Radio Frequency Power Measurements” Proceedings of the IEEE, vol. 55, No. 6, Jun. 1967 (Year: 1967). [cited by applicant]
Chung et al., “Entropy-Based Markov Chains for Multisensor Fusion” Journal of Intelligent and Robotic Systems 29: 161a189, 2000 (Year: 2000). [cited by applicant]
Scibelli et al., “Low-cost Stellar Sensor for Attitude Control of Small Satellites” 2019 Photonics & Electromagnetics Research Symposium—Spring (PIERS—SPRING), Rome, Italy, Jun. 17-20, (Year: 2019). [cited by applicant]
Tan et al., “A New Approach for Small Satellite Gyroscope and Star Tracker Fusion” Indian Journal of Science and Technology, vol. 9(17), May 2016 (Year: 2016). [cited by applicant]