CN117130029A - Positioning method of special vehicle positioning terminal and positioning terminal - Google Patents
Positioning method of special vehicle positioning terminal and positioning terminal Download PDFInfo
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/40—Correcting position, velocity or attitude
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/485—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an optical system or imaging system
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
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Abstract
The embodiment of the application discloses a positioning method of a special vehicle positioning terminal and the positioning terminal, wherein the method comprises the steps of collecting multi-source positioning data so as to match a plurality of positions of the special vehicle on a preset three-dimensional map based on the positioning data of each source; correcting the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles; based on a preset fusion algorithm, fusing a plurality of positions of the special vehicle to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time. The precise positioning of the special vehicle is realized by adjusting the multi-source positioning data in real time based on the environmental change, so that the problem of poor positioning precision of the special vehicle in the related technology is solved.
Description
Technical Field
The application relates to the technical field of computer information processing, in particular to a positioning method of a special vehicle positioning terminal and the positioning terminal.
Background
Special vehicles refer to vehicles which are limited by the design vehicle in terms of overall dimensions, weight and the like and are special for special purposes, are specially made or specially adapted and are provided with fixed device equipment, and are not motor vehicles for carrying people or goods. Special vehicles generally have special signs or special vehicle types. Refers to a vehicle which is responsible for special service and hangs special vehicle license plates, and is provided with an alarm and a marking lamp. Such as ambulances, fire-fighting vehicles, police vehicles, engineering rescue vehicles, military supervision vehicles, etc. Because special vehicles are different from common vehicles in tasks, and are vehicles with special purposes for executing emergency tasks, the emergency tasks are completed, and the time and the effort are very important, so that the special vehicles need to be monitored, and relevant information such as positions and the like is quickly acquired.
In the related art, when a special vehicle runs into a complex running environment, the positioning accuracy is not high, and the generated positioning error is large.
Disclosure of Invention
The application provides a positioning method of a special vehicle positioning terminal and the positioning terminal.
In a first aspect, the present application provides a positioning method of a vehicle positioning terminal, including collecting multi-source positioning data to match a plurality of positions of a special vehicle based on positioning data of each source on a preset three-dimensional map; correcting the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles; based on a preset fusion algorithm, fusing a plurality of positions of the special vehicle to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time.
Optionally, dynamically adjusting the fusion algorithm during the running of the special vehicle to obtain the target position of the special vehicle updated in real time includes: determining weight distribution information of each source in the fusion algorithm based on the accuracy of the acquired positioning data of each source of the special vehicle, wherein the accuracy is determined based on the positioning error model; generating a fusion algorithm updated in real time based on the weight distribution information; and determining the target position of the special vehicle based on the fusion algorithm updated in real time.
Optionally, before acquiring the multi-source positioning data, the method further comprises determining a positioning error model:
determining a satellite positioning error based on satellite positioning error = a-B log (number of visible satellites) -C satellite geometry factor-D signal blocking factor; wherein A is the inherent positioning error of the satellite positioning system; b is the descending speed of the positioning error caused by the increase of the number of visible satellites; c is a value set based on the influence of satellite geometry on positioning error; d is a value set based on the influence of signal shielding on positioning errors;
determining a base station positioning error based on a base station geographical distribution factor of square+c of the base station positioning error = a+b; wherein a is the inherent positioning error of the base station positioning system; b is the increase speed of the positioning error caused by the increase of the distance from the base station; c is a calculation formula set based on the influence of the geographic distribution of the base stations on the positioning error;
determining inertial navigation error based on inertial navigation error = δv+1/2 δa (Δt)/(2); wherein δv is the velocity error; δa is the acceleration error; Δt is the sampling time interval;
and/or determining that the visual positioning is error-free based on the visual positioning error = a-b ln (image resolution) +c feature points + d match accuracy, wherein a is a systematic inherent positioning error; b is the speed of improving the positioning accuracy caused by the improvement of the resolution; c is the increase of the positioning error caused by the increase of the feature points; d is the reduction of positioning errors caused by the improvement of the feature point matching accuracy.
Optionally, the method further comprises: acquiring actual parameters which are generated in the running process of the special vehicle and are the same as parameters indicated by the positioning error model; determining satellite positioning errors, base station positioning errors and inertial navigation errors based on the actual parameters respectively; the accuracy is determined based on the satellite positioning error, the base station positioning error, and the inertial navigation error.
Optionally, the method further comprises: and sending the target position of the special vehicle to a base station for forwarding to a server side in a preset internet of things platform through the base station, wherein the server side is configured to send the target position of the special vehicle to a user side.
In a second aspect, the present application provides a special vehicle positioning terminal, including an acquisition module configured to acquire multi-source positioning data to match a plurality of positions of a special vehicle based on positioning data of each source on a preset three-dimensional map; the correction module is configured to correct the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles; the fusion module is configured to fuse a plurality of positions of the special vehicle based on a preset fusion algorithm to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time.
Determining weight distribution information of each source in the fusion algorithm, wherein the size of the precision is determined based on the positioning error model;
generating a fusion algorithm updated in real time based on the weight distribution information;
and determining the target position of the special vehicle based on the fusion algorithm updated in real time.
Optionally, before acquiring the multi-source positioning data, the method further comprises determining a positioning error model:
determining a satellite positioning error based on satellite positioning error = a-B log (number of visible satellites) -C satellite geometry factor-D signal blocking factor; wherein A is the inherent positioning error of the satellite positioning system; b is the descending speed of the positioning error caused by the increase of the number of visible satellites; c is a value set based on the influence of satellite geometry on positioning error; d is a value set based on the influence of signal shielding on positioning errors;
determining a base station positioning error based on a base station geographical distribution factor of square+c of the base station positioning error = a+b; wherein a is the inherent positioning error of the base station positioning system; b is the increase speed of the positioning error caused by the increase of the distance from the base station; c is a calculation formula set based on the influence of the geographic distribution of the base stations on the positioning error;
determining inertial navigation error based on inertial navigation error = δv+1/2 δa (Δt)/(2); wherein δv is the velocity error; δa is the acceleration error; Δt is the sampling time interval;
and/or determining that the visual positioning is error-free based on the visual positioning error = a-b ln (image resolution) +c feature points + d match accuracy, wherein a is a systematic inherent positioning error; b is the speed of improving the positioning accuracy caused by the improvement of the resolution; c is the increase of the positioning error caused by the increase of the feature points; d is the reduction of positioning errors caused by the improvement of the feature point matching accuracy.
In a third aspect, the present application provides a computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of the first aspects.
In a fourth aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method according to any implementation manner of the first aspect when executing the program.
The positioning method of the special vehicle positioning terminal comprises the steps of collecting multi-source positioning data to match a plurality of positions of a special vehicle on a preset three-dimensional map based on the positioning data of each source; correcting the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles; based on a preset fusion algorithm, fusing a plurality of positions of the special vehicle to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time. The precise positioning of the special vehicle is realized by adjusting the multi-source positioning data in real time based on the environmental change, so that the problem of poor positioning precision of the special vehicle in the related technology is solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
Fig. 1 is a flowchart of a positioning method of a positioning terminal of a special vehicle according to an embodiment of the present application;
fig. 2 is an electronic device provided in an embodiment of the present application.
Detailed Description
Other advantages and advantages of the present application will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the technical features of the different embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
The system architecture suitable for the embodiment can comprise a positioning terminal, a base station and an internet of things platform (the platform can comprise a user side and a server side), all facilities can communicate through a narrowband internet of things (Narrow BandInternet of Things, NB-IoT), wherein the positioning terminal executes the method of the embodiment, positioning data of the positioning terminal is forwarded to the server side in the internet of things platform through the base station, and operations such as positioning, state monitoring and track playback are performed on a special vehicle on the user side of the platform, so that real-time monitoring on the special vehicle is achieved.
An embodiment of the present application provides a positioning method for a positioning terminal of a special vehicle, including:
step 101: multiple source positioning data are collected to match multiple positions of the special vehicle on a preset three-dimensional map based on the positioning data of the sources.
In this embodiment, the positioning terminal may be disposed on a special vehicle, and collect positioning data of different data sources through the acquisition module, which may include, but is not limited to, a satellite positioning data source, a base station positioning data source, an inertial navigation positioning data source, and a visual positioning data source.
Positioning data of each data source is acquired, the positioning data of each source is presented on a three-dimensional map, and matching can be performed based on the positioning data and the three-dimensional map data during presentation so as to match the positions of the positioning data of each source on the map on the three-dimensional map.
Step 102: and correcting the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles.
In this embodiment, since the running process of the special vehicle has environmental changes and the positioning data is error in different environments, it is very important to accurately determine the error of the positioning data of each source to correct the positioning data of each source to improve the positioning accuracy of the special vehicle.
Real-time errors of the positioning data of each source along with the environmental change in the positioning process can be respectively determined, then correction can be carried out based on the real-time errors, and the relatively accurate position can be determined.
Further, before a plurality of positions of the special vehicle are matched on the three-dimensional map based on the source positioning data, the positioning data can be corrected based on a preset positioning error model, then the corrected positioning data are matched into the three-dimensional map, and the matching points are corrected based on the difference between the matched matching points and the source positioning data.
Step 103: based on a preset fusion algorithm, fusing a plurality of positions of the special vehicle to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time.
In this embodiment, after different positions of the special vehicle are obtained, the multiple positions can be fused according to a preset algorithm, and when the special vehicle is implemented, the positioning accuracy of different sources in different environments can be evaluated, and then the weight coefficients of the different sources are adjusted based on the different accuracies, so that the advantages of the positioning sources in the environments are fully utilized, the special vehicle positioning accuracy is improved, and the special vehicle positioning accuracy is adapted to different environments.
As an optional implementation manner of this embodiment, dynamically adjusting the fusion algorithm during the running process of the special vehicle, so as to obtain the target position of the special vehicle updated in real time includes: determining weight distribution information of each source in the fusion algorithm based on the accuracy of the acquired positioning data of each source of the special vehicle, wherein the accuracy is determined based on the positioning error model; generating a fusion algorithm updated in real time based on the weight distribution information; and determining the target position of the special vehicle based on the fusion algorithm updated in real time.
In this optional implementation manner, a positioning error model may be set for the characteristics of each source, and the positioning error of each source may be determined based on the positioning error model, the positioning accuracy of each source may be estimated based on the positioning error, the weight coefficient of each source may be determined and adjusted based on the estimated positioning accuracy of different sources, so as to follow a new fusion algorithm in real time, and the target position of the special vehicle may be determined based on the fusion algorithm updated in real time. For example, in urban areas, the accuracy of base station positioning, and/or visual positioning, may be relatively high, so that the weight of base station positioning and/or visual positioning may be increased in that area; in jungle areas in mountainous areas, the positioning accuracy of satellite positioning and/or inertial navigation may be relatively high, so that the weights of satellite positioning and inertial navigation may be increased in the areas, that is, the weights are dynamically adjusted in different areas based on the positioning accuracy of different sources, and the magnitude of the weight adjustment are not limited herein.
As an optional implementation manner of this embodiment, before collecting the multi-source positioning data, the method further includes determining a positioning error model: determining a satellite positioning error based on satellite positioning error = a-B log (number of visible satellites) -C satellite geometry factor-D signal blocking factor; wherein A is the inherent positioning error of the satellite positioning system; b is the descending speed of the positioning error caused by the increase of the number of visible satellites; c is a value set based on the influence of satellite geometry on positioning error; d is a value set based on the influence of signal shielding on positioning errors; determining a base station positioning error based on a base station geographical distribution factor of square+c of the base station positioning error = a+b; wherein a is the inherent positioning error of the base station positioning system; b is the increase speed of the positioning error caused by the increase of the distance from the base station; c is a value set based on the influence of the geographic distribution of the base stations on the positioning error; determining inertial navigation error based on inertial navigation error = δv+1/2 δa (Δt)/(2); wherein δv—speed error; δa-acceleration error; Δt-sampling time interval; determining that the visual positioning is error-free based on the visual positioning error = a-b ln (image resolution) +c feature point number + d match accuracy, wherein a is a positioning error inherent to the system; b is the speed of improving the positioning accuracy caused by the improvement of the resolution; c is the increase of the positioning error caused by the increase of the feature points; d is the reduction of positioning errors caused by the improvement of the feature point matching accuracy.
The optional implementation mode provides a more accurate implementation mode for evaluating the positioning error of the satellite positioning system, the satellite positioning error can be determined through factors such as the number of visible satellites, geometric distribution of the satellites, signal shielding and the like, and in general, the more the number of visible satellites, the wider the geometric distribution, the less the signal shielding, and the higher the positioning precision.
Based on the above influencing factors, the following satellite positioning error model determination can be adopted:
satellite positioning error = a-B log (number of visible satellites) -C satellite geometry factor-D signal blocking factor, determining satellite positioning error; wherein A is the inherent positioning error of the satellite positioning system; b is the descending speed of the positioning error caused by the increase of the number of visible satellites; c is a value set based on the influence of satellite geometry on positioning error; d is a value set based on the influence of signal occlusion on the positioning error.
Geometric factors may include, but are not limited to: PDOP-position factor, which reflects the influence of satellite position on three-dimensional positioning precision; HDOP-level factor, which reflects the influence of satellite position on horizontal positioning accuracy; VDOP-vertical factor, which reflects the influence of satellite position on vertical positioning precision; TDOP-time factor: the influence reflecting satellite position on time positioning accuracy.
Occlusion factors include, but are not limited to: building shielding, namely shielding satellite signals by a high-rise building; terrain shading; the shield is used for shielding satellite signals in mountains, hills and other terrains; tree shielding, namely densely shielding satellite signals by trees; the tunnel shielding means that satellite signals cannot be received when entering the tunnel; weather shielding, namely fading of satellite signals caused by clouds, rain, snow and other severe weather.
Further, the positioning error of the base station can be determined by taking up the distance from the special vehicle, the geographic distribution of the base station and other factors. Based on the influencing factors, the following base station positioning error model determination can be adopted:
base station positioning error = a+b x square of distance + c x base station geographical distribution factor; wherein a is the inherent positioning error of the base station positioning system; b is the increase speed of the positioning error caused by the increase of the distance from the base station; c is a calculation formula set by the influence of the geographical distribution of the base stations on the positioning error.
Further, inertial navigation errors also exist for special vehicles, mainly from gyro drift and accelerometer drift, which accumulate over time, thus establishing the following inertial navigation error model:
inertial navigation error = δv+1/2 δa (Δt)/(2); wherein δv—speed error; δa-acceleration error; Δt-sampling time interval.
Furthermore, the visual positioning error mainly depends on camera parameters, feature point extraction precision, a matching algorithm and the like, and generally, the higher the image resolution is, the more feature points are abundant, the more accurate the matching algorithm is, and the smaller the positioning error is. Based on the factors, the visual positioning error model is established as follows:
visual positioning error = a-b ln (image resolution) +c feature points + d match accuracy; wherein a is the inherent positioning error of the system; b is the speed of improving the positioning accuracy caused by the improvement of the resolution; c is the increase of the positioning error caused by the increase of the feature points; d is the reduction of positioning errors caused by the improvement of the feature point matching accuracy.
By establishing the positioning error model, the positioning error of each source can be accurately determined, and the positioning precision of each source can be objectively and accurately estimated.
As an optional implementation manner of this embodiment, the method further includes: acquiring actual parameters which are generated in the running process of the special vehicle and are the same as parameters indicated by the positioning error model; determining satellite positioning errors, base station positioning errors and inertial navigation errors based on the actual parameters respectively; the accuracy is determined based on the satellite positioning error, the base station positioning error, and the inertial navigation error.
In this optional implementation manner, since the positioning error model is preset, the actual parameters corresponding to the factors in each preset positioning error model can be obtained in the actual running process of the special vehicle.
For example, the number of visible satellites that change in real time (may be every preset period) during the running of the special vehicle, the geometry of the satellites that change in real time (determined by the geometry factor of the satellites), the signal shielding factor that changes in real time, etc. are determined in real time, and this is taken as the actual parameter of the satellite positioning error model.
For example, the distance between the special vehicle and the base station, the geographic distribution factor of the base station, etc. which are changed in real time are determined in real time during the running process of the special vehicle, and the distance is taken as the actual parameter of the base station positioning error model.
For example, a speed error, an acceleration error, etc. of the inertial navigation are determined in real time during the running of the special vehicle, and this is taken as an actual parameter of the inertial navigation error model.
For example, the image resolution, the feature points, and the matching accuracy are determined in real time during the running of the special vehicle, and are used as actual parameters of the visual positioning error model.
After the actual parameters are acquired, the positioning errors of the positioning model can be determined, and the positioning accuracy of each source is based on the positioning errors.
As an optional implementation manner of this embodiment, the method further includes: and sending the target position of the special vehicle to a base station so as to be forwarded to a server side in a preset internet of things platform through the base station, wherein the server side is configured to send the target position of the special vehicle to a user side.
The method provided by one or more embodiments of the present application is based on the same idea, and the present application further provides a corresponding special vehicle positioning terminal, which includes an acquisition module configured to acquire multi-source positioning data, so as to match a plurality of positions of a special vehicle based on the positioning data of each source on a preset three-dimensional map;
the correction module is configured to correct the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles;
the fusion module is configured to fuse a plurality of positions of the special vehicle based on a preset fusion algorithm to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time.
Optionally, the fusion module is further configured to:
determining weight distribution information of each source in the fusion algorithm based on the accuracy of the acquired positioning data of each source of the special vehicle, wherein the accuracy is determined based on the positioning error model;
generating a fusion algorithm updated in real time based on the weight distribution information;
and determining the target position of the special vehicle based on the fusion algorithm updated in real time.
Optionally, before acquiring the multi-source positioning data, the method further comprises determining a positioning error model:
determining a satellite positioning error based on satellite positioning error = a-B log (number of visible satellites) -C satellite geometry factor-D signal blocking factor; wherein A is the inherent positioning error of the satellite positioning system; b is the descending speed of the positioning error caused by the increase of the number of visible satellites; c is a value set based on the influence of satellite geometry on positioning error; d is a value set based on the influence of signal shielding on positioning errors;
determining a base station positioning error based on a base station geographical distribution factor of square+c of the base station positioning error = a+b; wherein a is the inherent positioning error of the base station positioning system; b is the increase speed of the positioning error caused by the increase of the distance from the base station; c is a calculation formula set based on the influence of the geographic distribution of the base stations on the positioning error;
determining inertial navigation error based on inertial navigation error = δv+1/2 δa (Δt)/(2); wherein δv is the velocity error; δa is the acceleration error; Δt is the sampling time interval;
and/or determining that the visual positioning is error-free based on the visual positioning error = a-b ln (image resolution) +c feature points + d match accuracy, wherein a is a systematic inherent positioning error; b is the speed of improving the positioning accuracy caused by the improvement of the resolution; c is the increase of the positioning error caused by the increase of the feature points; d is the reduction of positioning errors caused by the improvement of the feature point matching accuracy.
Optionally, the method further comprises: acquiring actual parameters which are generated in the running process of the special vehicle and are the same as parameters indicated by the positioning error model; determining satellite positioning errors, base station positioning errors and inertial navigation errors based on the actual parameters respectively; the accuracy is determined based on the satellite positioning error, the base station positioning error, and the inertial navigation error.
Optionally, the method further comprises: and sending the target position of the special vehicle to a base station for forwarding to a server side in a preset internet of things platform through the base station, wherein the server side is configured to send the target position of the special vehicle to a user side.
The application also provides a schematic block diagram of the electronic device shown in fig. 2, which corresponds to fig. 1. At the hardware level, as shown in fig. 2, the electronic device includes a processor, an internal bus, a network interface, a memory, and a nonvolatile storage, and may of course include hardware required by other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement a model loading method as described above with respect to fig. 1. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present application, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (AlteraHardware Description Language), confluence, CUPL (Cornell UniversityProgramming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit HardwareDescription Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific IntegratedCircuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable media (including but not limited to disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable media (including but not limited to disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer media including memory storage devices.
The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. The positioning method of the special vehicle positioning terminal is characterized by comprising the following steps of:
acquiring multi-source positioning data to match a plurality of positions of a special vehicle on a preset three-dimensional map based on the positioning data of each source;
correcting the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles;
based on a preset fusion algorithm, fusing a plurality of positions of the special vehicle to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time.
2. The positioning method of a positioning terminal of a special vehicle according to claim 1, wherein dynamically adjusting the fusion algorithm during running of the special vehicle to obtain a real-time updated target position of the special vehicle comprises:
determining weight distribution information of each source in the fusion algorithm based on the accuracy of the acquired positioning data of each source of the special vehicle, wherein the accuracy is determined based on the positioning error model;
generating a fusion algorithm updated in real time based on the weight distribution information;
and determining the target position of the special vehicle based on the fusion algorithm updated in real time.
3. The method of locating a special vehicle locating terminal of claim 2, wherein prior to collecting the multi-source locating data, the method further comprises determining a locating error model:
determining a satellite positioning error based on satellite positioning error = a-B log (number of visible satellites) -C satellite geometry factor-D signal blocking factor; wherein A is the inherent positioning error of the satellite positioning system; b is the descending speed of the positioning error caused by the increase of the number of visible satellites; c is a value set based on the influence of satellite geometry on positioning error; d is a value set based on the influence of signal shielding on positioning errors;
determining a base station positioning error based on a base station geographical distribution factor of square+c of the base station positioning error = a+b; wherein a is the inherent positioning error of the base station positioning system; b is the increase speed of the positioning error caused by the increase of the distance from the base station; c is a calculation formula set based on the influence of the geographic distribution of the base stations on the positioning error;
determining inertial navigation error based on inertial navigation error = δv+1/2 δa (Δt)/(2); wherein δv is the velocity error; δa is the acceleration error; Δt is the sampling time interval;
and/or determining that the visual positioning is error-free based on the visual positioning error = a-b ln (image resolution) +c feature points + d match accuracy, wherein a is a systematic inherent positioning error; b is the speed of improving the positioning accuracy caused by the improvement of the resolution; c is the increase of the positioning error caused by the increase of the feature points; d is the reduction of positioning errors caused by the improvement of the feature point matching accuracy.
4. The positioning method of a special vehicle positioning terminal according to claim 3, characterized in that the method further comprises:
acquiring actual parameters which are generated in the running process of the special vehicle and are the same as parameters indicated by the positioning error model;
determining satellite positioning errors, base station positioning errors and inertial navigation errors based on the actual parameters respectively;
the accuracy is determined based on the satellite positioning error, the base station positioning error, and the inertial navigation error.
5. The positioning method of a special vehicle positioning terminal according to claim 1, characterized in that the method further comprises:
and sending the target position of the special vehicle to a base station for forwarding to a server side in a preset internet of things platform through the base station, wherein the server side is configured to send the target position of the special vehicle to a user side.
6. A special vehicle positioning terminal, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is configured to acquire multi-source positioning data so as to match a plurality of positions of a special vehicle on a preset three-dimensional map based on the positioning data of each source;
the correction module is configured to correct the positions of the special vehicles based on a preset positioning error model to obtain a plurality of corrected positions of the special vehicles;
the fusion module is configured to fuse a plurality of positions of the special vehicle based on a preset fusion algorithm to obtain a target position of the special vehicle, wherein the fusion algorithm is dynamically adjusted in the running process of the special vehicle to obtain the target position of the special vehicle updated in real time.
7. The special vehicle positioning terminal of claim 6, wherein the fusion module is further configured to:
determining weight distribution information of each source in the fusion algorithm based on the accuracy of the acquired positioning data of each source of the special vehicle, wherein the accuracy is determined based on the positioning error model;
generating a fusion algorithm updated in real time based on the weight distribution information;
and determining the target position of the special vehicle based on the fusion algorithm updated in real time.
8. The special vehicle positioning terminal of claim 6, wherein prior to collecting the multi-source positioning data, the method further comprises determining a positioning error model:
determining a satellite positioning error based on satellite positioning error = a-B log (number of visible satellites) -C satellite geometry factor-D signal blocking factor; wherein A is the inherent positioning error of the satellite positioning system; b is the descending speed of the positioning error caused by the increase of the number of visible satellites; c is a value set based on the influence of satellite geometry on positioning error; d is a value set based on the influence of signal shielding on positioning errors;
determining a base station positioning error based on a base station geographical distribution factor of square+c of the base station positioning error = a+b; wherein a is the inherent positioning error of the base station positioning system; b is the increase speed of the positioning error caused by the increase of the distance from the base station; c is a calculation formula set based on the influence of the geographic distribution of the base stations on the positioning error;
determining inertial navigation error based on inertial navigation error = δv+1/2 δa (Δt)/(2); wherein δv is the velocity error; δa is the acceleration error; Δt is the sampling time interval;
and/or determining that the visual positioning is error-free based on the visual positioning error = a-b ln (image resolution) +c feature points + d match accuracy, wherein a is a systematic inherent positioning error; b is the speed of improving the positioning accuracy caused by the improvement of the resolution; c is the increase of the positioning error caused by the increase of the feature points; d is the reduction of positioning errors caused by the improvement of the feature point matching accuracy.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-5.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-5 when executing the program.
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| CN202311110905.XA CN117130029A (en) | 2023-08-31 | 2023-08-31 | Positioning method of special vehicle positioning terminal and positioning terminal |
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| CN202311110905.XA CN117130029A (en) | 2023-08-31 | 2023-08-31 | Positioning method of special vehicle positioning terminal and positioning terminal |
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