Air-ground cooperative positioning method suitable for low-altitude vehicle networking technology

Abstract

According to the air-ground cooperative positioning method suitable for the low-altitude vehicle networking technology, the advantages of the vehicle networking technology and the low-altitude intelligent networking technology are comprehensively utilized, and the air-ground cooperative positioning method can be used in the complex traffic environment that positioning signals of urban viaducts, urban canyons and the like are shielded. A target vehicle carries out information interaction with surrounding vehicles and low-altitude aircrafts through wireless transmission, vehicle position information is estimated in real time through an intelligent algorithm based on information fusion of GPS / Beidou positioning, intelligent network connection wireless distance measurement and the like, three-dimensional position information is subjected to positioning enhancement, continuous positioning information is provided, the positioning precision is improved, and the positioning accuracy is improved. The function of continuous and accurate positioning when the vehicle positioning signal is shielded is realized. According to the invention, low-cost positioning and ranging modules are adopted at the same time, compared with the existing multi-sensor fusion positioning technology such as a vehicle-mounted laser radar, the cost is lower, the application prospect is better, and technical support can be provided for accurate and continuous positioning of vehicles in a future intelligent network connection three-dimensional traffic environment.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent networked transportation and low-altitude intelligent network technology, and specifically relates to an air-ground collaborative positioning method suitable for low-altitude vehicle network technology. Background Art

[0002] With the continuous development of GPS / Beidou satellite navigation and positioning, Internet of Vehicles, and low-altitude intelligent Internet, the demand for accuracy and continuity of vehicle positioning in future three-dimensional traffic environments is becoming more and more urgent. In an intelligent network environment, cars and low-altitude aircraft can more easily sense and obtain their respective positions and relative distance information, providing technical support for air-ground collaborative positioning.

[0003] At present, vehicle positioning mainly adopts GPS / Beidou satellite positioning or satellite / inertial navigation combined positioning and other methods. In the actual driving environment, vehicle-mounted satellite positioning is easily disturbed by complex environments, and there are errors from multiple sources, and the positioning accuracy needs to be improved; although satellite / inertial navigation combined positioning can improve positioning accuracy to a certain extent, there is a cumulative drift of errors in inertial navigation, and it is difficult to continuously and effectively correct the positioning error. In the complex urban traffic environment, when the vehicle-mounted positioning signal is blocked by the environment such as viaducts and urban canyons, the existing satellite and combined positioning still cannot guarantee the accurate and continuous positioning of the vehicle. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an air-to-ground collaborative positioning method suitable for low-altitude vehicle networking technology, which is used for continuous and accurate positioning when the vehicle positioning signal is blocked.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: an air-ground collaborative positioning method suitable for low-altitude vehicle networking technology, comprising the following steps:

[0006] S1: Real-time acquisition of real-time location information of intelligent connected vehicles and cooperatively positioned unmanned aerial vehicles;

[0007] S2: Measure and calculate the angle and straight-line distance between the intelligent connected vehicle and the cooperatively positioned unmanned aerial vehicle;

[0008] S3: The continuous positioning error is optimized by using the signal estimation algorithm, and the collaborative positioning error is made to meet or approach the application requirements through the Gauss-Newton iteration method.

[0009] According to the above scheme, in step S1, the specific steps are: the sensor for obtaining the position information includes a GPS or Beidou positioning module; the coordinate position of the intelligent networked vehicle is obtained as The coordinate position of the collaboratively positioned unmanned aerial vehicle is

[0010] According to the above scheme, in step S2, the yaw angle of the intelligent networked vehicle to the cooperatively positioned unmanned aerial vehicle i is measured by the ultra-wideband signal UWB as α i , the pitch angle is β i , the straight-line distance is D t .

[0011] According to the above scheme, in step S2, the coordinate position of the intelligent networked vehicle is calculated based on the geometric relationship between the unmanned aerial vehicle and the intelligent networked vehicle. for:

[0012]

[0013] According to the above scheme, in step S3,

[0014] The co-localization observation vector is expressed as:

[0015] O=[α i ,β i ,D ij ] T ;

[0016] The measured value taking into account the error is estimated as:

[0017]

[0018] Will Represented as the position V of the intelligent connected vehicle t Function

[0019]

[0020] set up The transfer matrix is ​​H, and the measurement error is It is expressed as:

[0021] δ=H·dV t .

[0022] Furthermore, in step S3, the transfer matrix H is represented as the Jacobian matrix

[0023] Furthermore, in step S3, let n be the number of iterations, ΔV t (t) is the vehicle position at time t The increment of iterative calculation error Set the iteration threshold T, and stop the iteration if δ<T, so that the error approaches the minimum value. The optimized vehicle position at time t is calculated as:

[0024]

[0025] (ΔV t (t+1)) n =(V t (t)) n +(ΔV t (t)) n .

[0026] A system for air-ground collaborative positioning applicable to low-altitude vehicle networking technology, the system comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps of an air-ground collaborative positioning method applicable to low-altitude vehicle networking technology.

[0027] Further, it includes intelligent connected vehicles, collaboratively positioned unmanned aerial vehicles and satellite positioning systems.

[0028] A computer storage medium stores a computer program that can be executed by a computer processor, and the computer program executes an air-ground collaborative positioning method suitable for low-altitude vehicle networking technology.

[0029] The beneficial effects of the present invention are:

[0030] 1. The present invention is an air-ground collaborative positioning method suitable for low-altitude vehicle networking technology, which comprehensively utilizes the advantages of vehicle networking and low-altitude intelligent networking technology. In complex traffic environments such as urban viaducts and urban canyons where positioning signals are blocked, through information interaction between low-altitude aircraft and vehicles, the position and distance information of the low-altitude aircraft are integrated to perform collaborative positioning calculations on the target vehicle, enhance the positioning of the three-dimensional position information, provide continuous positioning information and improve positioning accuracy, thereby realizing the function of continuous and accurate positioning when the vehicle positioning signal is blocked.

[0031] 2. In the intelligent network environment, the target vehicle of the present invention exchanges information with surrounding vehicles and low-altitude aircraft (drones, flying cars, etc.) through wireless transmission. Based on the fusion of GPS / Beidou positioning, intelligent network wireless ranging and other information, the vehicle position information is estimated in real time through an intelligent algorithm to improve the collaborative positioning method and system for improving the positioning accuracy when the vehicle positioning signal is blocked in complex urban traffic environments.

[0032] 3. The present invention adopts low-cost positioning and ranging modules at the same time, which is lower in cost than the existing multi-sensor fusion positioning technologies such as vehicle-mounted lidar, has better application prospects, and can provide technical support for the accurate and continuous positioning of vehicles in future intelligent networked three-dimensional traffic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1is a schematic diagram of an embodiment of the present invention.

[0034] Figure 2 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0036] See also Figure 1 and Figure 2 , an embodiment of the present invention comprises the following steps:

[0037] S1: Use GPS / Beidou positioning module to obtain real-time location information of intelligent connected vehicles and cooperatively positioned unmanned aerial vehicles, such as Figure 1 As shown, the coordinate position of the intelligent connected vehicle is expressed as:

[0038]

[0039] The position of the co-located UAV is expressed as:

[0040]

[0041] S2: Obtain the yaw angle α of the intelligent connected vehicle to the cooperatively positioned unmanned aerial vehicle i based on UWB signal measurement and calculation i , pitch angle β i and the straight-line distance D t ;

[0042] When the vehicle's positioning signal is blocked, the position information of the unmanned aerial vehicle whose low-altitude positioning signal is not blocked is used to perform collaborative position solution based on the geometric relationship between the unmanned aerial vehicle and the target vehicle to determine the position of the intelligent connected vehicle.

[0043]

[0044] S3: The continuous positioning error is optimized by using the signal estimation algorithm, and the collaborative positioning error is made to meet or approach the application requirements through the Gauss-Newton iteration method;

[0045] The co-localization observation vector can be expressed as:

[0046] 0=[α i ,β i ,D ij ] T ;

[0047] The measured value taking into account the error is estimated as:

[0048]

[0049] Will Represented as the connected vehicle position V t Function

[0050]

[0051] Measurement error It can be expressed as:

[0052] δ=H·dV t ;

[0053] in, The transfer matrix H can be expressed as the Jacobian matrix

[0054] Iterative calculation error Set the iteration threshold T (set according to the positioning accuracy requirements and experience value), if δ<T, stop the iteration, so that the error approaches the minimum value, and the optimized vehicle position at time t can be calculated:

[0055]

[0056] (ΔV t (t+1)) n =(V t (t)) n +(ΔV t (t)) n ;

[0057] Where n is the number of iterations, ΔV t (t) is the vehicle position at time t increment.

[0058] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0059] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. An air-ground collaborative positioning method applicable to low-altitude vehicle networking technology, characterized in that: The following steps are involved: S1: Real-time acquisition of real-time location information of intelligent connected vehicles and cooperatively positioned unmanned aerial vehicles; S2: Measure and calculate the angle and straight-line distance between the intelligent connected vehicle and the cooperatively positioned unmanned aerial vehicle; S3: The continuous positioning error is optimized by using the signal estimation algorithm, and the collaborative positioning error is made to meet or approach the application requirements through the Gauss-Newton iteration method.

2. The air-ground collaborative positioning method applicable to low-altitude vehicle networking technology according to claim 1 is characterized in that: In the step S1, the specific steps are: the sensor for acquiring the position information includes a GPS or Beidou positioning module; the coordinate position of the intelligent networked vehicle is obtained as The coordinate position of the collaboratively positioned unmanned aerial vehicle is 3. The air-ground collaborative positioning method applicable to low-altitude vehicle networking technology according to claim 1 is characterized in that: In step S2, the yaw angle of the intelligent connected vehicle to the cooperatively positioned unmanned aerial vehicle i is measured by the ultra-wideband signal UWB as α i , the pitch angle is β i , the straight-line distance is D t .

4. The air-ground collaborative positioning method applicable to low-altitude vehicle networking technology according to claim 1 is characterized in that: In step S2, the coordinate position of the intelligent networked vehicle is calculated based on the geometric relationship between the unmanned aerial vehicle and the intelligent networked vehicle. for:

5. The air-ground collaborative positioning method applicable to low-altitude vehicle networking technology according to claim 1 is characterized in that: In the step S3, The co-localization observation vector is expressed as: O=[a i ,b i ,D ij ] T ; The measured value taking into account the error is estimated as: Will Represented as the position V of the intelligent connected vehicle t Function set up The transfer matrix is ​​H, and the measurement error is It is expressed as: δ=H·dV t 。 6. The air-ground collaborative positioning method applicable to low-altitude vehicle networking technology according to claim 5 is characterized by: In step S3, the transfer matrix H is represented as the Jacobian matrix 7. The air-ground collaborative positioning method applicable to low-altitude vehicle networking technology according to claim 5 is characterized by: In step S3, let n be the number of iterations, ΔV t (t) is the vehicle position at time t The increment of iterative calculation error Set the iteration threshold T, and stop the iteration if δ<T, so that the error approaches the minimum value. The optimized vehicle position at time t is calculated as: (ΔV t (t+1)) n =(V t (t)) n +(ΔV t (t)) n 。 8. An air-ground collaborative positioning system suitable for low-altitude vehicle networking technology, the system comprising a processor and a memory, characterized in that: The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of an air-ground collaborative positioning method suitable for low-altitude vehicle networking technology as described in any one of claims 1 to 7.

9. The air-ground collaborative positioning system applicable to low-altitude vehicle networking technology according to claim 8 is characterized in that: Including intelligent connected vehicles, collaboratively positioned unmanned aerial vehicles and satellite positioning systems.

10. A computer storage medium, characterized in that: A computer program executable by a computer processor is stored therein, and the computer program executes an air-ground collaborative positioning method applicable to low-altitude vehicle networking technology as described in any one of claims 1 to 7.

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