Air-ground integrated navigation method and system based on UWB and multi-mode fusion
By using a navigation method that integrates UWB and multimodal fusion, a three-segment precise path is generated. By combining heterogeneous networks and Kalman filtering algorithms, the problems of insufficient collaborative scheduling and positioning accuracy in traditional air-to-ground navigation systems are solved, achieving efficient integrated air-to-ground navigation and carbon emission reduction incentives.
Patent Information
- Application Number
- CN202510926788.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-07
- Publication Date
- 2025-11-11
AI Technical Summary
Traditional air-to-ground navigation systems lack efficient collaborative scheduling mechanisms in densely populated urban areas, resulting in time-consuming path planning, insufficient positioning accuracy, and a lack of carbon emission reduction incentive mechanisms, making it difficult to achieve seamless integration of ground parking and low-altitude flight and green traffic management.
A navigation method integrating UWB and multimodal approaches is adopted. A three-segment accurate path is generated through a multimodal path planning engine. High-precision positioning is achieved by combining a heterogeneous network of UWB base stations and Bluetooth beacons. Kalman filtering algorithm is used to suppress multipath effects, and a carbon integral linkage model is constructed for reliable evidence storage and incentive.
It has achieved a parking space matching accuracy of ≥95%, a flight route planning error of ≤±10 meters, and a positioning accuracy improved to ±0.3 meters. It has also achieved accurate carbon emission reduction calculation, improved resource scheduling efficiency and positioning accuracy, and promoted the construction of a green transportation ecosystem.
Smart Images
Figure CN120926973A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of intelligent transportation technology, specifically relating to an integrated air-ground navigation method and system based on UWB and multimodal fusion. Background Technology
[0002] In the field of intelligent transportation, traditional air-to-ground navigation systems face multiple technical bottlenecks: On the one hand, the coordinated scheduling of ground vehicles and low-altitude aircraft in densely populated urban areas lacks an efficient integration mechanism. Existing path planning technologies struggle to achieve seamless integration between ground parking and low-altitude flight, resulting in low efficiency in air-to-ground resource scheduling. This is particularly evident in parking space matching and take-off / landing point planning, where issues such as large coordinate errors and lengthy path planning times exist. On the other hand, positioning stability is insufficient in complex environments. Traditional positioning technologies are prone to drift in scenarios involving multipath effects and signal obstruction. For example, GPS signals attenuate severely in parking lots, and the accuracy of ordinary UWB positioning drops sharply due to environmental noise interference, failing to meet navigation requirements at the ±0.5 meter level. Furthermore, existing systems lack quantitative incentive mechanisms for low-carbon travel. The carbon emission reductions from ground parking and low-altitude flight cannot be linked for calculation and reliable verification, hindering the construction of a green transportation ecosystem.
[0003] To address the aforementioned issues, this invention proposes an integrated air-ground navigation method and system based on UWB and multimodal fusion: A multimodal path planning engine dynamically couples a parking lot digital twin model with low-altitude airway data to generate a precise three-segment path: parking space - take-off / landing point - flight target point, simultaneously achieving a parking space matching accuracy of ≥95% and a flight route planning error of ≤±10 meters. Utilizing a heterogeneous network composed of UWB base stations and Bluetooth beacons, combined with a time-division multiplexing anti-interference protocol and a Kalman filter algorithm, positioning accuracy is improved to ±0.3 meters, effectively suppressing multipath effects. An innovative carbon credit linkage model is constructed, using blockchain technology to reliably store data such as electric vehicle parking time and electric aircraft flight distance, achieving a storage success rate of ≥99%. This enables the quantitative calculation and trading incentives for carbon emission reductions, thereby overcoming the technical bottlenecks in resource scheduling, positioning accuracy, and green management in urban three-dimensional transportation. Summary of the Invention
[0004] The purpose of this invention is to provide an integrated air-ground navigation method and system based on UWB and multimodal fusion, aiming to solve the problems in the prior art.
[0005] To achieve the above objectives, the present invention provides the following technical solution:
[0006] An integrated air-ground navigation method, characterized in that it includes:
[0007] Receive user navigation requests and parse vehicle type, target location, and low-altitude service identifier;
[0008] The parking lot digital twin model is called to match the coordinates of vacant parking spaces and the nearest take-off and landing points. The parking space matching accuracy of this model is no less than 95%, and the error of the take-off and landing point coordinates is controlled within ±5 meters.
[0009] Generate a three-segment path using a multimodal path engine:
[0010] a) From vehicle entrance to parking space (ground navigation), route planning takes into account real-time traffic conditions, and the planning time does not exceed 10 seconds;
[0011] b) From parking space to take-off and landing point (pedestrian navigation), combined with pedestrian flow data, generate an efficient walking route;
[0012] c) From the take-off and landing point to the low-altitude target point (flight route), based on the low-altitude route data, the aircraft safety separation requirements shall be met, and the flight route planning error shall not exceed ±10 meters;
[0013] Real-time vehicle / aircraft positioning based on UWB beacon clusters, dynamic path offset correction, and positioning update frequency of no less than 10Hz;
[0014] Based on the navigation phase and vehicle type, billing is conducted using a dynamic pricing model, and carbon credit vouchers are generated, with a billing error not exceeding ±0.5%.
[0015] As a preferred embodiment of the present invention, the UWB positioning enhancement includes:
[0016] A heterogeneous network consisting of UWB base stations and Bluetooth 5.1 beacons is deployed in parking lots and take-off and landing points. The coverage radius of UWB base stations is no less than 50 meters, and the coverage radius of Bluetooth 5.1 beacons is no less than 20 meters.
[0017] The time division multiplexing (TDMA) protocol is used to allocate beacon communication time slots, suppressing signal collisions, and the time slot allocation success rate is no less than 98%.
[0018] By fusing UWB Time Difference of Arrival (TDoA) data with IMU inertial data using a Kalman filter, the positioning accuracy is improved to within ±0.3 meters.
[0019] As a preferred embodiment of the present invention, the UWB anti-interference mechanism specifically includes:
[0020] When the multipath effect intensity is detected to be >3dB, the beacon switching protocol is initiated, and the three beacons with the strongest signals are automatically connected, with a switching time of no more than 0.5 seconds.
[0021] Based on the path loss model PL(d)=PL0+10nlog10(d)+Xσ, the distance is dynamically calibrated (where Xσ is the environmental noise variable), and the distance calibration error does not exceed ±0.2 meters.
[0022] As a preferred embodiment of the present invention, the carbon integral linkage model is implemented as follows: the parking time Tp of the electric vehicle and the flight distance Df of the electric aircraft are collected, and the data collection error does not exceed ±1%; the carbon integral is calculated according to the formula C=α·Tp+β·Df (α=0.5kgCO2 / h, β=0.2kgCO2 / km);
[0023] Carbon credits are written to the blockchain for evidence storage and linked to user accounts, with a success rate of no less than 99%.
[0024] As a preferred embodiment of the present invention, it includes:
[0025] A multimodal path planning module (for executing any one of the methods of claims 1-4), with a module response time not exceeding 5 seconds;
[0026] The UWB positioning enhancement module (including UWB base station group, Bluetooth beacon and filter processor) has a positioning accuracy of ±0.3 meters for UWB base station group and ±1 meter for Bluetooth beacon.
[0027] The carbon credit management module (connected to the blockchain node) has a data synchronization delay of no more than 1 second.
[0028] Compared with the prior art, the beneficial effects of the present invention are:
[0029] 1. In this solution, three-segment path generation is accurate: the multimodal path engine integrates the parking lot digital twin model and low-altitude air route data to achieve a parking space matching accuracy of 95%, take-off and landing point coordinate error of ±5 meters, flight route planning error of ±10 meters, and ground navigation planning time of ≤10 seconds, which is 40% more efficient than the traditional solution.
[0030] Real-time data dynamic optimization: Route planning incorporates real-time traffic conditions, pedestrian flow, and low-altitude airway traffic data to automatically avoid congested areas and no-fly zones. For example, ground navigation can avoid real-time congested road sections, reducing travel time by 15%. Flight routes meet aircraft safety separation requirements to avoid the risk of mid-air collisions.
[0031] Highly efficient and collaborative module response: The multimodal path planning module has a response time of ≤5 seconds and interacts with the UWB positioning enhancement module in real time to exchange positioning data (update frequency 10Hz), dynamically correcting path deviations and ensuring seamless navigation throughout the entire process.
[0032] 2. In this solution, high-precision positioning via heterogeneous network: UWB base stations (coverage radius ≥ 50 meters) and Bluetooth beacons (coverage ≥ 20 meters) form a heterogeneous network, achieving a 98% time slot allocation success rate through the TDMA protocol and suppressing signal conflicts; the Kalman filter fuses TDoA and IMU data, achieving a positioning accuracy of ±0.3 meters, which is 60% higher than traditional UWB.
[0033] Anti-interference mechanism dynamic calibration: When the multipath effect intensity is >3dB, the beacon switching protocol connects the three strongest beacons within 0.5 seconds, and dynamically calibrates the distance (error ±0.2 meters) in combination with the path loss model. For example, in a densely packed area of parking lot pillars, the positioning drift amplitude is reduced from ±0.8 meters to within ±0.3 meters.
[0034] Real-time location data updates: A 10Hz location update frequency ensures real-time path correction in high-speed moving scenarios. For example, when an aircraft is flying, the flight path can be adjusted in real time to avoid sudden obstacles, with a path correction delay of ≤0.1 seconds. Attached Figure Description
[0035] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0036] Figure 1 This is an overall flowchart of the air-ground integrated navigation method of the present invention;
[0037] Figure 2 This is a flowchart illustrating the implementation of the carbon integral linkage model of the present invention. Detailed Implementation
[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0039] Example 1
[0040] Please see Figures 1-2 The present invention provides the following technical solutions:
[0041] An integrated air-ground navigation method, characterized in that it includes:
[0042] Receive user navigation requests and parse vehicle type, target location, and low-altitude service identifier;
[0043] The parking lot digital twin model is called to match the coordinates of vacant parking spaces and the nearest take-off and landing points. The parking space matching accuracy of this model is no less than 95%, and the error of the take-off and landing point coordinates is controlled within ±5 meters.
[0044] Generate a three-segment path using a multimodal path engine:
[0045] a) From vehicle entrance to parking space (ground navigation), route planning takes into account real-time traffic conditions, and the planning time does not exceed 10 seconds;
[0046] b) From parking space to take-off and landing point (pedestrian navigation), combined with pedestrian flow data, generate an efficient walking route;
[0047] c) From the take-off and landing point to the low-altitude target point (flight route), based on the low-altitude route data, the aircraft safety separation requirements shall be met, and the flight route planning error shall not exceed ±10 meters;
[0048] Real-time vehicle / aircraft positioning based on UWB beacon clusters, dynamic path offset correction, and positioning update frequency of no less than 10Hz;
[0049] Based on the navigation phase and vehicle type, billing is conducted using a dynamic pricing model, and carbon credit vouchers are generated, with a billing error not exceeding ±0.5%.
[0050] In a specific embodiment of the present invention, when a user issues a navigation request through the client, the request information is transmitted to the core processing module of the system. The core processing module parses the vehicle type, target location, and low-altitude service identifier to clarify the user's needs. Subsequently, the module calls the parking lot digital twin model, which quickly matches available parking spaces and the coordinates of the nearest take-off and landing point based on real-time parking space occupancy data and surrounding take-off and landing point information, achieving a parking space matching accuracy of no less than 95% and controlling the take-off and landing point coordinate error within ±5 meters.
[0051] After receiving the matching results, the multimodal path engine initiates path planning. For ground navigation from the vehicle entrance to the parking space, the path engine acquires real-time traffic data such as road congestion and construction, and plans an efficient ground driving route within 10 seconds. For pedestrian navigation from the parking space to the take-off and landing point, it combines pedestrian flow monitoring data to avoid densely populated areas and generate convenient walking routes. For flight route planning from the take-off and landing point to the low-altitude target point, it ensures that the planned flight route meets the aircraft safety separation requirements and the error does not exceed ±10 meters based on real-time traffic flow and no-fly zones of the low-altitude airway.
[0052] During navigation, the UWB beacon cluster maintains real-time communication with the positioning terminals on vehicles and aircraft, acquiring location information at a positioning update frequency of no less than 10Hz. Once a path deviation is detected, the positioning terminal feeds the data back to the multimodal path engine, which then dynamically corrects the path. Simultaneously, the billing module calculates charges based on the navigation stage (ground, walking, flight) and vehicle type using a dynamic pricing model. It also uses a carbon credit linkage model to collect data on electric vehicle parking time and electric aircraft flight distance, calculating carbon credits and ultimately generating data containing billing information and carbon credit vouchers, completing the entire navigation service process.
[0053] Please refer to the details. Figures 1-2 The UWB positioning enhancement includes:
[0054] A heterogeneous network consisting of UWB base stations and Bluetooth 5.1 beacons is deployed in parking lots and take-off and landing points. The coverage radius of UWB base stations is no less than 50 meters, and the coverage radius of Bluetooth 5.1 beacons is no less than 20 meters.
[0055] The time division multiplexing (TDMA) protocol is used to allocate beacon communication time slots, suppressing signal collisions, and the time slot allocation success rate is no less than 98%.
[0056] By fusing UWB Time Difference of Arrival (TDoA) data with IMU inertial data using a Kalman filter, the positioning accuracy is improved to within ±0.3 meters.
[0057] In this embodiment: In parking lots and take-off and landing points, a heterogeneous network is pre-deployed according to the standard that the coverage radius of UWB base stations is no less than 50 meters and the coverage radius of Bluetooth 5.1 beacons is no less than 20 meters. When a vehicle or aircraft enters the area, the UWB base station and Bluetooth beacon in the UWB positioning enhancement module start working. Through the Time Division Multiplexing (TDMA) protocol, communication time slots are allocated to the beacons with a time slot allocation success rate of no less than 98%, suppressing signal collisions. At the same time, the filter processor uses a Kalman filter to fuse UWB Time Difference of Arrival (TDoA) data with IMU inertial data, improving the positioning accuracy to within ±0.3 meters, providing accurate location information for multimodal path planning.
[0058] Please refer to the details. Figures 1-2 The UWB anti-interference mechanism specifically includes:
[0059] When the multipath effect intensity is detected to be >3dB, the beacon switching protocol is initiated, and the three beacons with the strongest signals are automatically connected, with a switching time of no more than 0.5 seconds.
[0060] Based on the path loss model PL(d)=PL0+10nlog10(d)+Xσ, the distance is dynamically calibrated (where Xσ is the environmental noise variable), and the distance calibration error does not exceed ±0.2 meters.
[0061] In this embodiment: During navigation, the UWB positioning enhancement module continuously monitors the signal status. Once a multipath effect intensity >3dB is detected, the UWB anti-interference mechanism is immediately activated, executing a beacon switching protocol within 0.5 seconds and automatically connecting to the three beacons with the strongest signals. Simultaneously, based on the path loss model PL(d)=PL0+10nlog10(d)+Xσ, the distance is dynamically calibrated, controlling the distance calibration error within ±0.2 meters, ensuring the stability and accuracy of positioning, and ensuring that the multimodal path engine obtains reliable position data for path correction.
[0062] Please refer to the details. Figure 2The carbon integral linkage model is implemented as follows: the parking time Tp of the electric vehicle and the flight distance Df of the electric aircraft are collected, and the data collection error does not exceed ±1%; the carbon integral is calculated according to the formula C=α·Tp+β·Df (α=0.5kgCO2 / h, β=0.2kgCO2 / km);
[0063] Carbon credits are written to the blockchain for evidence storage and linked to user accounts, with a success rate of no less than 99%.
[0064] In this embodiment: During the user's use of the navigation service, the system collects the electric vehicle's parking time Tp and the electric aircraft's flight distance Df in real time, with a data collection error not exceeding ±1%. When navigation ends, the carbon credit management module calculates the carbon credits according to the formula C = α·Tp + β·Df (α = 0.5 kg CO2 / h, β = 0.2 kg CO2 / km), and writes the calculation results to the blockchain for notarization, linking them to the user's account with a notarization success rate of no less than 99%. During this process, the multimodal path planning module provides navigation stage and vehicle type information to assist the carbon credit linkage model in accurate calculation.
[0065] Please refer to the details. Figures 1-2 ,include:
[0066] A multimodal path planning module (for executing any one of the methods of claims 1-4), with a module response time not exceeding 5 seconds;
[0067] The UWB positioning enhancement module (including UWB base station group, Bluetooth beacon and filter processor) has a positioning accuracy of ±0.3 meters for UWB base station group and ±1 meter for Bluetooth beacon.
[0068] The carbon credit management module (connected to the blockchain node) has a data synchronization delay of no more than 1 second.
[0069] In this embodiment: the multimodal path planning module receives user navigation requests with a response time of no more than 5 seconds and executes path planning-related methods. The UWB positioning enhancement module provides high-precision positioning data, with UWB base station group positioning accuracy reaching ±0.3 meters and Bluetooth beacon positioning accuracy reaching ±1 meter. The carbon credit management module connects to the blockchain node and, after completing carbon credit calculation, updates the stored information to the blockchain with a data synchronization delay of no more than 1 second. All modules collaborate with each other; the UWB positioning enhancement module and anti-interference mechanism ensure accurate positioning, the multimodal path planning module achieves efficient path planning, and the carbon credit management module completes carbon credit-related operations, jointly realizing an integrated air-ground navigation service.
[0070] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. An integrated air-ground navigation method, characterized in that, include: Receive user navigation requests and parse vehicle type, target location, and low-altitude service identifier; The parking lot digital twin model is called to match the coordinates of vacant parking spaces and the nearest take-off and landing points. The parking space matching accuracy of this model is no less than 95%, and the error of the take-off and landing point coordinates is controlled within ±5 meters. Generate a three-segment path using a multimodal path engine: a) From vehicle entrance to parking space (ground navigation), route planning takes into account real-time traffic conditions, and the planning time does not exceed 10 seconds; b) From parking space to take-off and landing point (pedestrian navigation), combined with pedestrian flow data, generate an efficient walking route; c) From the take-off and landing point to the low-altitude target point (flight route), based on the low-altitude route data, the aircraft safety separation requirements shall be met, and the flight route planning error shall not exceed ±10 meters; Real-time vehicle / aircraft positioning based on UWB beacon clusters, dynamic path offset correction, and positioning update frequency of no less than 10Hz; Based on the navigation phase and vehicle type, billing is conducted using a dynamic pricing model, and carbon credit vouchers are generated, with a billing error not exceeding ±0.5%.
2. The method according to claim 1, characterized in that, The UWB positioning enhancement includes: A heterogeneous network consisting of UWB base stations and Bluetooth 5.1 beacons is deployed in parking lots and take-off and landing points. The coverage radius of UWB base stations is no less than 50 meters, and the coverage radius of Bluetooth 5.1 beacons is no less than 20 meters. The time division multiplexing (TDMA) protocol is used to allocate beacon communication time slots, suppressing signal collisions, and the time slot allocation success rate is no less than 98%. By fusing UWB Time Difference of Arrival (TDoA) data with IMU inertial data using a Kalman filter, the positioning accuracy is improved to within ±0.3 meters.
3. The air-to-ground integrated navigation method and system based on UWB and multimodal fusion according to claim 2, characterized in that: The UWB anti-interference mechanism specifically includes: When the multipath effect intensity is detected to be >3dB, the beacon switching protocol is initiated, and the three beacons with the strongest signals are automatically connected, with a switching time of no more than 0.5 seconds. Based on the path loss model PL(d)=PL0+10nlog10(d)+Xσ, the distance is dynamically calibrated (where Xσ is the environmental noise variable), and the distance calibration error does not exceed ±0.2 meters.
4. The air-to-ground integrated navigation method and system based on UWB and multimodal fusion according to claim 3, characterized in that: The carbon integral linkage model is implemented as follows: the parking time Tp of the electric vehicle and the flight distance Df of the electric aircraft are collected, and the data collection error does not exceed ±1%; the carbon integral is calculated according to the formula C=α·Tp+β·Df (α=0.5kgCO2 / h, β=0.2kgCO2 / km); Carbon credits are written to the blockchain for evidence storage and linked to user accounts, with a success rate of no less than 99%.
5. An integrated air-to-ground navigation system, characterized in that, include: A multimodal path planning module (for executing any one of the methods of claims 1-4), with a module response time not exceeding 5 seconds; The UWB positioning enhancement module (including UWB base station group, Bluetooth beacon and filter processor) has a positioning accuracy of ±0.3 meters for UWB base station group and ±1 meter for Bluetooth beacon. The carbon credit management module (connected to the blockchain node) has a data synchronization delay of no more than 1 second.