CN116202534B - Tunnel positioning method, device, equipment and storage medium - Google Patents
Tunnel positioning method, device, equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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Abstract
The application discloses a method, a device, equipment and a storage medium for positioning in a tunnel, which relate to the field of indoor positioning and machine learning and comprise the following steps: determining a radio signal fresnel zone from the remote radio device; in the process of receiving and transmitting signals between the remote radio equipment and the motion carrier, determining the initial position of the motion carrier according to the channel state information of the current Fresnel zone of the remote radio equipment; optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation by utilizing the optimized Kalman filter based on position information determined by a radio and position information determined by a strapdown inertial navigation system to obtain motion carrier positioning information. The output of the remote radio equipment and the strapdown inertial navigation are used as the input quantity of the Kalman filtering, the Kalman filter parameters are corrected through deep learning, then the optimal estimation is carried out, the problem of unstable strapdown inertial navigation is solved, and accurate and stable positioning information is obtained.
Description
Technical Field
The present invention relates to the field of indoor positioning and machine learning, and in particular, to a method, an apparatus, a device, and a storage medium for positioning in a tunnel.
Background
The outdoor positioning technology generally uses a global satellite navigation system (GNSS, global Navigation Satellite System), but in places where tunnels or high-rise forests exist, the GNSS signals are frequently lost, so that the positioning cannot be performed for a long time, and at this time, high-precision position information needs to be obtained through other ways, so that the existing positioning technology, the vision and millimeter wave radar technology, has an unsatisfactory effect under the condition of complex dark environment and topography environment. Conventional inertial navigation systems in the prior art, such as strapdown inertial navigation system (SINS, strap-down Inertial Navigation System), measure and store motion state data and environmental characteristics of a carrier during movement of the carrier, and transmit stored information to a carrier computer, and obtain velocity and displacement of the carrier by integrating acceleration of the carrier, and obtain attitude angle of movement of the carrier from angular velocity data through an attitude calculation algorithm, but there is a serious problem in that the system diverges without the assistance of other sensors, and errors increase with time. Therefore, how to reduce the problem of unstable positioning caused by errors of the strapdown inertial navigation system and obtain more accurate positioning information is a problem to be solved in the art.
Disclosure of Invention
In view of the above, the present invention aims to provide a positioning method, apparatus, device and storage medium for a tunnel, which can obtain accurate and stable positioning information of a moving carrier in the tunnel by using standard output of a remote radio device and a strapdown inertial navigation system as input of a deep learning kalman filter, optimizing parameters of the kalman filter by the deep learning, and combining the two systems by the optimized kalman filter. The specific scheme is as follows:
in a first aspect, the present application provides a positioning method in a tunnel, including:
determining a to-be-positioned area covered by a long-distance radio signal Fresnel zone of long-distance radio equipment according to the position of the long-distance radio equipment arranged in a preset tunnel range; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device;
in the process that the remote radio equipment carries out remote radio signal receiving and transmitting with a moving carrier in the area to be positioned, determining the initial position of the moving carrier according to the channel state information of the current remote radio signal Fresnel zone of the remote radio equipment;
Optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier by utilizing the optimized Kalman filter so as to obtain positioning information of the motion carrier; the first location information is determined based on the initial location.
Optionally, after the remote radio device performs remote radio signal transceiving with the moving carrier in the area to be located, the method further includes:
and performing time synchronization according to the first signal receiving time of the moving carrier for receiving the transmitting signal of the radio signal transmitting device and the second signal receiving time of the radio signal receiving device for receiving the moving carrier return signal, so as to determine depth position information of the moving carrier in the tunnel by utilizing a micro control unit based on a time synchronization result.
Optionally, the determining the initial position of the moving carrier according to the channel state information of the fresnel zone of the current long-range radio signal of the long-range radio device includes:
Establishing a search space based on the depth position information, and matching channel state information corresponding to a Fresnel zone of a current remote radio signal in the search space with a preset information base to determine target channel state information of a moving carrier in the area to be positioned;
and determining the transverse position information of the moving carrier in the tunnel according to the target channel state information, and determining the initial position of the moving carrier based on the depth position information and the transverse position information.
Optionally, the determining the initial position of the moving carrier according to the channel state information of the fresnel zone of the current long-range radio signal of the long-range radio device further includes:
respectively communicating with a remote radio signal transmitting device and a remote radio signal receiving device by utilizing the moving carrier in the area to be positioned;
and determining a signal angle during communication by using a directional antenna of the remote radio equipment, and determining the initial position of the motion carrier according to the signal angle and channel state information of a Fresnel zone of a current remote radio signal of the remote radio equipment.
Optionally, the performing, by using the optimized kalman filter, kalman optimal estimation based on the first location information determined by the remote radio device and the second location information determined by the strapdown inertial navigation system of the moving carrier, to obtain positioning information of the moving carrier includes:
establishing a tunnel positioning system model based on a remote radio system and a strapdown inertial navigation system according to the Kalman filter after machine learning and optimization;
and carrying out corresponding Kalman optimal estimation by utilizing the tunnel positioning system model, a corresponding observation equation and an error equation and based on the first position information determined by the remote radio equipment and the second position information determined by the strapdown inertial navigation system of the moving carrier.
Optionally, the positioning method in the tunnel further includes:
and if the long-distance radio signal of the long-distance radio equipment fails to be received, carrying out reasoning on a preset time period through a pre-trained TinyML model after the long-distance radio signal fails to be received, so as to control the position error of the strapdown inertial navigation system based on a reasoning result.
In a second aspect, the present application provides an in-tunnel positioning device, including:
The area determining module is used for determining an area to be positioned, which is covered by a long-distance radio signal Fresnel zone of the long-distance radio equipment, according to the positions of the long-distance radio equipment arranged in the range of the preset tunnel; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device;
the position determining module is used for determining the initial position of the moving carrier according to the channel state information of the Fresnel zone of the current remote radio signal of the remote radio device in the process of receiving and transmitting the remote radio signal between the remote radio device and the moving carrier in the area to be positioned;
the position optimization module is used for optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier by utilizing the Kalman filter after optimization so as to obtain positioning information of the motion carrier; the first location information is determined based on the initial location.
Optionally, the location determining module includes:
and the remote radio communication sub-module is used for respectively communicating with the remote radio signal transmitting equipment and the remote radio signal receiving equipment by utilizing the moving carrier in the area to be positioned, determining the signal angle during communication by utilizing the directional antenna of the remote radio equipment, and determining the initial position of the moving carrier according to the signal angle and the channel state information of the current remote radio signal Fresnel area of the remote radio equipment.
In a third aspect, the present application provides an electronic device comprising a processor and a memory; the memory is used for storing a computer program, and the computer program is loaded and executed by the processor to realize the tunnel positioning method.
In a fourth aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the tunnel locating method described above.
In the method, a to-be-positioned area covered by a long-distance radio signal Fresnel zone of long-distance radio equipment is determined according to the positions of the long-distance radio equipment arranged in a preset tunnel range; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device; in the process that the remote radio equipment carries out remote radio signal receiving and transmitting with a moving carrier in the area to be positioned, determining the initial position of the moving carrier according to the channel state information of the current remote radio signal Fresnel zone of the remote radio equipment; optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier by utilizing the Kalman filter after optimization so as to obtain positioning information of the motion carrier. Therefore, the initial position information can be obtained by transmitting and receiving signals through the remote radio system according to the channel state information in the Fresnel zone, then the Kalman optimal estimation is carried out on the first position information determined by the initial position and the second position information of the SINS, the positioning information obtained by the SINS is corrected through the remote radio system, the error influence generated by the strapdown inertial navigation system is weakened, more accurate position information is obtained, and the initialization parameters of the Kalman filter are optimized through the deep learning model, so that the Kalman filtering convergence speed is improved, the speed for determining the position information of the motion carrier is faster, and the accuracy is higher.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a positioning method in a tunnel provided by the present application;
FIG. 2 is a schematic diagram of a long range radio system positioning provided herein;
FIG. 3 is a flowchart of a specific positioning method in a tunnel provided in the present application;
FIG. 4 is a schematic diagram of a fusion system of a long-range radio system and a strapdown inertial navigation system provided by the present application;
fig. 5 is a schematic structural diagram of an in-tunnel positioning device provided in the present application;
fig. 6 is a block diagram of an electronic device provided in the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The existing positioning technology, vision and millimeter wave radar technology have unsatisfactory effects under the condition of dark environment and complex terrain environment. And strapdown inertial navigation systems diverge without other sensor assistance, with errors increasing over time. According to the method and the device, the initial position can be obtained through the Fresnel zone, the positioning information obtained by the SINS is corrected through the remote radio system, the error influence generated by the strapdown inertial navigation system is weakened, more accurate position information is obtained, and the initialization parameters of the Kalman filter are optimized through the deep learning model, so that the accuracy of determining the position information of the motion carrier is higher.
Referring to fig. 1, the embodiment of the invention discloses a positioning method in a tunnel, which comprises the following steps:
step S11, determining a to-be-positioned area covered by a long-distance radio signal Fresnel zone of the long-distance radio equipment according to the positions of the long-distance radio equipment arranged in a preset tunnel range; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device.
In this embodiment, first, a to-be-localized area covered by a remote radio signal fresnel zone of a remote radio device is determined according to the positions of the remote radio devices arranged within a preset tunnel range. It will be appreciated that the Long Range Radio described above is a related device that performs positioning based on the Long Range Radio (LoRa) technology. The LoRa is a low-power consumption local area network wireless standard developed by semtech company, is an Internet of things Modulation technology of linear frequency Modulation spread spectrum, is also called as broadband phenomenon frequency Modulation (Chirp Modulation) technology, and has the greatest characteristics that the distance of the LoRa is farther than the distance of the LoRa transmitted by other wireless modes under the same power consumption condition, the low power consumption and the long distance are unified, the LoRa is 3 to 5 times longer than the traditional wireless radio frequency communication distance under the same power consumption, and compared with the traditional Modulation technology, the LoRa has longer transmission distance under the same power consumption and has strong anti-interference capability. In this way, by arranging the LoRa transmitting and receiving equipment in the tunnel range, the equipment position is adjusted so that the LoRa signal Fresnel zone covers the positioning area, so that the mobile carrier to be positioned can judge the approximate geographical position of the mobile carrier before entering the tunnel through communication between the LoRa equipment and the tunnel device, and a proper information base is selected for matching in the next step. It should be noted that the moving carrier may be a vehicle or other carrier on which the positioning device in the tunnel is mounted, and the information base is used for performing state matching based on the channel state information of the fresnel zone of the LoRa signal, so as to further determine the relevant position information of the moving carrier.
And step S12, determining the initial position of the moving carrier according to the channel state information of the Fresnel zone of the current remote radio signal of the remote radio equipment in the process of receiving and transmitting the remote radio signal between the remote radio equipment and the moving carrier in the area to be positioned.
In this embodiment, it is necessary to determine the initial position of the moving carrier during the process of transmitting and receiving the signal from the remote radio transmitting device to and from the remote radio device after the moving carrier enters the tunnel and performing remote radio signal transmission and reception with the moving carrier in the area to be positioned.
In a specific embodiment, the time synchronization may be performed according to a first signal receiving time of the moving carrier receiving the transmitting signal of the radio signal transmitting device and a second signal receiving time of the radio signal receiving device receiving the moving carrier return signal, so as to determine the depth of entry into the tunnel by the arrival time of the two signals, that is, determine the depth position information of the moving carrier in the tunnel by using the micro control unit based on the time synchronization result. After the depth position information of the moving carrier is determined, a search space is established based on the depth position information, channel state information corresponding to the Fresnel zone of the current remote radio signal in the search space is matched with the preset information base determined in the step S11, so that target channel state information of the moving carrier in the area to be positioned is determined, transverse position information of the moving carrier in the tunnel is determined according to the target channel state information, and the initial position of the moving carrier is determined based on the depth position information and the transverse position information.
The specific flow diagram of determining the initial position of the moving carrier through the depth position information and the transverse position information is shown in fig. 2, the moving carrier sends a signal to the receiving device after entering the tunnel, receives the signal from the transmitting device, the transmitting device and the receiving device are in time synchronization, and the depth of entering the tunnel is determined through the arrival time of the two signals; CSI (Channel State Information ) is then obtained in a fresnel zone maintained between the transmitting and receiving devices, and since the fresnel zone is an elliptical zone formed by signals between the transmitting and receiving devices in which the fluctuations of tangential signals conforming to the elliptical direction are greatest when blocked, the lateral position information of the moving carrier in the tunnel can be determined by the CSI. In fig. 2, a rectangular area is set as a to-be-positioned area, an arc is a fresnel area formed by transmitting and receiving equipment, a triangle symbol is a motion carrier to be positioned, after the motion carrier receives information, a signal arrival time difference and a phase angle are calculated through information propagation by respectively communicating with a transmitting station and a receiving station of a remote radio, the distance between the motion carrier and the remote radio signal equipment is calculated through a micro control unit (Microcontroller Unit, MCU) carried on the motion carrier, the fuzzy position of the motion carrier is preliminarily determined, a search space is established at the fuzzy position, and the CSI in the space is searched and matched. Because the signal passes through different reflection path lengths or path fading in the propagation process, the signal is always abnormal, the abnormal information is contained in the CSI information stored in the information base corresponding to the LoRa, and the corresponding CSI matching point, namely the position coordinates of the motion carrier, is quickly found through machine learning analysis on the CSI.
In another specific embodiment, the moving carrier in the area to be positioned can be used for respectively communicating with the remote radio signal transmitting device and the remote radio signal receiving device, then the signal angle during communication is determined by using the moving carrier and the directional antenna of the remote radio device, and the initial position of the moving carrier is determined according to the signal angle and the channel state information of the current remote radio signal fresnel area of the remote radio device. In this way, in this embodiment, by calculating the ambiguous position of the motion carrier by transmitting and receiving parameters of the signal, and then performing further feature matching based on the Channel State Information (CSI) of the fresnel zone of the LoRa signal, more accurate position information can be obtained.
Step S13, optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation by utilizing the Kalman filter after optimization based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier so as to obtain positioning information of the motion carrier; the first location information is determined based on the initial location.
In this embodiment, considering the position coordinates obtained by the LoRa technology, there is a certain error due to the influence of information transmission, and the output frequency is low, and the combination with another positioning technology is needed to obtain positioning information with higher precision and frequency, so that the error problem is solved by the combined positioning technology of LoRa and SINS. Specifically, initializing parameters of a Kalman filter are optimized through a deep learning model, and Kalman optimal estimation is performed by utilizing the optimized Kalman filter based on the initial position and position information determined by a strapdown inertial navigation system of the motion carrier, so that final positioning information of the motion carrier is obtained. It will be appreciated that the initial position is a range position, rather than a specific position, and that the initial position may be further determined by the remote radio device as the first position information after determining the initial position by the channel state of the fresnel zone. Moreover, it should be noted that, since the signal of the remote radio device may have power outage or other unexpected situations to cause interruption of the received signal, only the SINS is in operation, and the accumulated error exists after the signal interruption, and the position error is accumulated for a long time, so if the remote radio signal of the remote radio device fails to receive, the inference of the preset time period is performed by the pre-trained tinylml model after the remote radio signal fails to receive, so as to control the position error of the strapdown inertial navigation system based on the inference result, and ensure that the error of the SINS does not increase sharply in a short time, so that the accuracy of the obtained positioning information can be ensured.
In addition, it should be noted that, the hardware part of the positioning device adopted by the tunnel positioning method in this embodiment is a high-precision accelerometer, a gyroscope, a magnetometer sensor, an MCU processor, and a LoRa radio frequency communication module are hardware platforms, and the built tunnel positioning device. The gyroscope, the accelerometer and the magnetometer are used for carrying out strapdown inertial navigation calculation to obtain the position and the speed information of the SINS; the LoRa radio frequency communication module is used for sensing radio frequency signals by the positioning device, calculating fuzzy positions by transmitting and receiving various parameters of the signals, and then carrying out further feature matching based on channel state information of a Fresnel zone of the LoRa signals to obtain more accurate position information; the MCU processor is mainly used for controlling each module and processing the collected data through a core algorithm, including sensor data collection, filtering and processing, fusion algorithm resolving, machine learning algorithm reasoning and the like.
Through the technical scheme, the embodiment can determine the to-be-positioned area covered by the remote radio signal Fresnel zone of the remote radio equipment according to the positions of the remote radio equipment arranged in the preset tunnel range; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device; in the process that the remote radio equipment carries out remote radio signal receiving and transmitting with a moving carrier in the area to be positioned, determining remote radio signal receiving time and signal phase according to channel state information of a current remote radio signal Fresnel zone of the remote radio equipment, or determining an initial position of the moving carrier through a signal arrival angle; and optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier by utilizing the Kalman filter after optimization so as to obtain positioning information of the motion carrier. Therefore, the initial position information can be obtained by transmitting and receiving signals through the remote radio system according to the channel state information in the Fresnel zone, then the Kalman optimal estimation is carried out on the first position information determined by the initial position and the second position information of the SINS, the positioning information obtained by the SINS is corrected through the remote radio system, the error influence generated by the strapdown inertial navigation system is weakened, more accurate position information is obtained, and the initialization parameters of the Kalman filter are optimized through the deep learning model, so that the Kalman filtering convergence speed is improved, and the accuracy of determining the position information of the motion carrier is higher.
Based on the above embodiment, the present application can determine the position information of the motion vector according to the combined positioning technique of the LoRa and the SINS, and the fusion system of the LoRa and the SINS will be described in detail in the present embodiment. Referring to fig. 3, an embodiment of the present application discloses a specific positioning method in a tunnel, including:
step S21, determining a to-be-positioned area covered by a long-distance radio signal Fresnel zone of the long-distance radio equipment according to the positions of the long-distance radio equipment arranged in a preset tunnel range; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device.
And S22, determining the initial position of the moving carrier according to the channel state information of the Fresnel zone of the current remote radio signal of the remote radio equipment in the process of receiving and transmitting the remote radio signal between the remote radio equipment and the moving carrier in the area to be positioned.
And S23, optimizing initialization parameters of a Kalman filter through a deep learning model, and establishing a tunnel positioning system model based on the Kalman filter after machine learning and optimization based on a remote radio system and a strapdown inertial navigation system.
In this embodiment, the position coordinates obtained by the LoRa technology have a certain error and have a low output frequency, and the Strapdown Inertial Navigation System (SINS) measures three-position acceleration and three-dimensional angular velocity of the object in the inertial reference system through the sensor, and integrates the three-position acceleration and the three-dimensional angular velocity through the mathematical method, so as to calculate the real-time position, velocity, attitude and other data of the object. The method and the device can obtain the high-frequency position and information of the object by using the strapdown inertial navigation system, and can be completed by self without inputting external active information, so that the initialization parameters of the Kalman filter are optimized by the deep learning model, and a tunnel positioning system model is built on the basis of the remote radio system and the strapdown inertial navigation system according to the machine learning and the optimized Kalman filter. In this way, the positioning information of the LoRa system is corrected through the SINS system, a system model of the LoRa and the SINS is established by combining machine learning and Kalman filtering technology, the problem that the navigation position of a fast moving object is not updated timely due to low output frequency of CSI identification in the LoRa system can be eliminated according to short-term stability and high-frequency characteristics of the strapdown inertial navigation system, and the accuracy and the output frequency of positioning data are improved.
And step S24, utilizing the tunnel positioning system model, the corresponding observation equation and the error equation, and carrying out corresponding Kalman optimal estimation based on the first position information determined by the remote radio equipment and the second position information determined by the strapdown inertial navigation system of the moving carrier.
In this embodiment, as shown in a schematic diagram of a fusion system of a remote radio system and a strapdown inertial navigation system in fig. 4, based on the shown scheme, a tunnel positioning system model, a corresponding observation equation and an error equation are utilized, and based on an initial position and position information determined by the strapdown inertial navigation system of a moving carrier, a corresponding kalman optimal estimation is performed to obtain final positioning information of the moving carrier. Specifically, in this embodiment, a rectangular coordinate system composed of x parallel to the tunnel direction, y perpendicular to the tunnel direction, and h is selected as a navigation coordinate system, 17 state parameters are adopted in a state equation, and specific state quantities are as follows:
。
in the above, the installation angle error isThe speed error isThe position error is +.>. The gyroscope error isAccelerometer error of +.>The frequency error is，/>Distance error calculated from time error of radio frequency signal,/- >Is a radio frequencyThe resulting phase error is equivalent to the distance error. The state equation thus combined is:
。
wherein,,representing a state transition matrix>Indicating the error amount of the system state +.>Is a system process noise.
Taking the difference between the position and speed calculated by the SINS and the position and speed of the LoRa as a measurement value Z, the measurement equation can be expressed as follows:
。
in the method, in the process of the invention,nonlinear observation equation representing position, velocity, +.>Representing white noise.
From the foregoing steps, the integrated navigation discrete system can be expressed as:
。
wherein,,representation->State transition matrix of time->Representation->State vector of time of day->Representing the observation equation->Representation->Measurement vector of time,/->And->Representing gaussian white noise and being uncorrelated with each other. In the Kalman filtering, the initial value can influence the convergence rate of the whole system, and different initial values exist under different environments to enable the convergence rate of the Kalman filtering to be optimal, so that data are collected under different environments to train through constructing a deep learning model, and when a device in a tunnel works, quick initialization work is carried out according to the trained tunnel positioning system model, and the convergence rate of the system is improved.
In this embodiment, according to the positions of the remote radio devices arranged within the preset tunnel range, determining a to-be-positioned area covered by a remote radio signal fresnel zone of the remote radio devices; and in the process that the remote radio equipment carries out remote radio signal receiving and transmitting with the moving carrier in the area to be positioned, determining the initial position of the moving carrier according to the channel state information of the current remote radio signal Fresnel zone of the remote radio equipment. Optimizing initialization parameters of a Kalman filter through a deep learning model, and establishing a tunnel positioning system model based on a remote radio system and a strapdown inertial navigation system according to machine learning and the Kalman filter after optimization. And carrying out corresponding Kalman optimal estimation based on the initial position and the position information determined by the strapdown inertial navigation system of the motion carrier by utilizing the tunnel positioning system model, the corresponding observation equation and the error equation. It can be understood that the position information obtained through the long-distance radio system has long-term stability, but because of instability of signals and ambiguity solution of matched positions, the position information is easy to generate small-range mutation, so that positioning errors are large, meanwhile, because the CSI identification is obtained by reasoning after deep learning training, the matching speed is low, the output frequency is low, and the problem that navigation position update is not timely exists for fast moving objects is solved. Therefore, a model constructed by the strapdown inertial navigation system and the remote radio signal system is established by combining machine learning and Kalman filtering technology, then continuous iteration is carried out on the two data through an optimal estimation algorithm based on an observation equation and an error equation of LoRa and SINS, an optimal estimation value is obtained, and finally high-precision high-frequency positioning data is output.
For more specific processing procedures in the steps S21 and S22, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no detailed description is given here.
In this application, referring to fig. 5, the embodiment of the present application further discloses an in-tunnel positioning device, including:
a region determining module 11, configured to determine a region to be located covered by a long-range radio signal fresnel region of a long-range radio device according to a position of the long-range radio device disposed within a preset tunnel range; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device;
a position determining module 12, configured to determine, during the process of transceiving a remote radio signal between the remote radio device and a moving carrier in the area to be located, an initial position of the moving carrier according to channel state information of a fresnel zone of a current remote radio signal of the remote radio device;
the position optimization module 13 is configured to optimize initialization parameters of a kalman filter through a deep learning model, and perform kalman optimal estimation based on first position information determined by the remote radio device and second position information determined by a strapdown inertial navigation system of the motion carrier by using the kalman filter after optimization, so as to obtain positioning information of the motion carrier; the first location information is determined based on the initial location.
The location determining module 12 specifically includes:
and the remote radio communication sub-module is used for respectively communicating with the remote radio signal transmitting equipment and the remote radio signal receiving equipment by utilizing the moving carrier in the area to be positioned, determining the signal angle during communication by utilizing the directional antenna of the remote radio equipment, and determining the initial position of the moving carrier according to the signal angle and the channel state information of the current remote radio signal Fresnel area of the remote radio equipment.
In this embodiment, according to the positions of the remote radio devices arranged within the preset tunnel range, determining a to-be-positioned area covered by a remote radio signal fresnel zone of the remote radio devices; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device; in the process that the remote radio equipment carries out remote radio signal receiving and transmitting with a moving carrier in the area to be positioned, determining the initial position of the moving carrier according to the channel state information of the current remote radio signal Fresnel zone of the remote radio equipment; optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier by utilizing the Kalman filter after optimization so as to obtain positioning information of the motion carrier. Therefore, according to the technical scheme, the initial position information can be obtained by transmitting and receiving signals through the remote radio system according to the channel state information in the Fresnel zone, then the Kalman optimal estimation is carried out on the first position information determined by the initial position and the second position information of the SINS, the positioning information obtained by the SINS is corrected through the remote radio system, the error influence generated by the strapdown inertial navigation system is weakened, more accurate position information is obtained, and the initialization parameters of the Kalman filter are optimized through the deep learning model, so that the Kalman filtering convergence speed is improved, the speed for determining the position information of the motion carrier is faster, and the accuracy is higher.
In some specific embodiments, the location determination module 12 further comprises:
and the depth position information determining unit is used for carrying out time synchronization according to the first signal receiving time of the moving carrier for receiving the transmitting signal of the radio signal transmitting device and the second signal receiving time of the radio signal receiving device for receiving the moving carrier return signal, so as to determine the depth position information of the moving carrier in the tunnel by utilizing the micro control unit based on the time synchronization result.
In some specific embodiments, the location determination module 12 specifically includes:
the channel state information determining unit is used for establishing a search space based on the depth position information, and matching channel state information corresponding to the Fresnel zone of the current remote radio signal in the search space with a preset information base so as to determine target channel state information of the moving carrier in the area to be positioned;
and the first initial position determining unit is used for determining the transverse position information of the moving carrier in the tunnel according to the target channel state information and determining the initial position of the moving carrier based on the depth position information and the transverse position information.
In some specific embodiments, the location determination module 12 further comprises:
a device communication unit for communicating with a remote radio signal transmitting device and a remote radio signal receiving device, respectively, using the moving carrier in the region to be localized;
and the second initial position determining unit is used for determining a signal angle during communication by using the directional antenna of the remote radio equipment and determining the initial position of the moving carrier according to the signal angle and the channel state information of the Fresnel zone of the current remote radio signal of the remote radio equipment.
In some specific embodiments, the location optimization module 13 specifically includes:
the model building unit is used for building a tunnel positioning system model based on the remote radio system and the strapdown inertial navigation system according to the Kalman filter after machine learning and optimization;
and the Kalman estimation unit is used for carrying out corresponding Kalman optimal estimation by utilizing the tunnel positioning system model, the corresponding observation equation and the error equation and based on the first position information determined by the remote radio equipment and the second position information determined by the strapdown inertial navigation system of the moving carrier.
In some specific embodiments, the positioning device in the tunnel further comprises:
and the error control unit is used for carrying out reasoning on a preset time period through a pre-trained TinyML model after the long-distance radio signal reception fails if the long-distance radio signal reception of the long-distance radio equipment fails, so as to control the position error of the strapdown inertial navigation system based on a reasoning result.
Further, the embodiment of the present application further discloses an electronic device, and fig. 6 is a structural diagram of the electronic device 20 according to an exemplary embodiment, where the content of the drawing is not to be considered as any limitation on the scope of use of the present application.
Fig. 6 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is configured to store a computer program that is loaded and executed by the processor 21 to implement the relevant steps in the tunnel locating method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and computer programs 222, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the in-tunnel positioning method performed by the electronic device 20 disclosed in any of the previous embodiments.
Further, the application also discloses a computer readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the previously disclosed in-tunnel localization method. For specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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.
The foregoing has outlined the detailed description of the preferred embodiment of the present application, and the detailed description of the principles and embodiments of the present application has been provided herein by way of example only to facilitate the understanding of the method and core concepts of the present application; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (8)
1. A method of positioning in a tunnel, comprising:
determining a to-be-positioned area covered by a long-distance radio signal Fresnel zone of long-distance radio equipment according to the position of the long-distance radio equipment arranged in a preset tunnel range; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device;
in the process that the remote radio equipment carries out remote radio signal receiving and transmitting with a moving carrier in the area to be positioned, determining the initial position of the moving carrier according to the channel state information of the current remote radio signal Fresnel zone of the remote radio equipment;
Optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier by utilizing the optimized Kalman filter so as to obtain positioning information of the motion carrier; the first location information is determined based on the initial location;
wherein after the remote radio device performs remote radio signal transceiving with the motion carrier in the area to be positioned, the method further comprises:
performing time synchronization according to a first signal receiving time of the moving carrier for receiving a transmitting signal of the radio signal transmitting device and a second signal receiving time of the radio signal receiving device for receiving a return signal of the moving carrier, so as to determine depth position information of the moving carrier in a tunnel by utilizing a micro control unit based on a time synchronization result;
correspondingly, the determining the initial position of the motion carrier according to the channel state information of the Fresnel zone of the current long-range radio signal of the long-range radio device comprises:
Establishing a search space based on the depth position information, and matching channel state information corresponding to a Fresnel zone of a current remote radio signal in the search space with a preset information base to determine target channel state information of a moving carrier in the area to be positioned;
and determining the transverse position information of the moving carrier in the tunnel according to the target channel state information, and determining the initial position of the moving carrier based on the depth position information and the transverse position information.
2. The tunneling positioning method of claim 1, wherein said determining an initial position of said moving carrier based on channel state information of a current long-range radio signal fresnel zone of said long-range radio device further comprises:
respectively communicating with a remote radio signal transmitting device and a remote radio signal receiving device by utilizing the moving carrier in the area to be positioned;
and determining a signal angle during communication by using a directional antenna of the remote radio equipment, and determining the initial position of the motion carrier according to the signal angle and channel state information of a Fresnel zone of a current remote radio signal of the remote radio equipment.
3. The method for positioning a moving carrier in a tunnel according to claim 1, wherein the performing kalman optimal estimation by using the optimized kalman filter based on the first position information determined by the remote radio device and the second position information determined by the strapdown inertial navigation system of the moving carrier to obtain positioning information of the moving carrier includes:
establishing a tunnel positioning system model based on a remote radio system and a strapdown inertial navigation system according to the Kalman filter after machine learning and optimization;
and carrying out corresponding Kalman optimal estimation by utilizing the tunnel positioning system model, a corresponding observation equation and an error equation and based on the first position information determined by the remote radio equipment and the second position information determined by the strapdown inertial navigation system of the moving carrier.
4. A positioning method in a tunnel according to any one of claims 1 to 3, further comprising:
and if the long-distance radio signal of the long-distance radio equipment fails to be received, carrying out reasoning on a preset time period through a pre-trained TinyML model after the long-distance radio signal fails to be received, so as to control the position error of the strapdown inertial navigation system based on a reasoning result.
5. An in-tunnel positioning device, comprising:
the area determining module is used for determining an area to be positioned, which is covered by a long-distance radio signal Fresnel zone of the long-distance radio equipment, according to the positions of the long-distance radio equipment arranged in the range of the preset tunnel; the long-range radio device includes a long-range radio signal transmitting device and a long-range radio signal receiving device;
the position determining module is used for determining the initial position of the moving carrier according to the channel state information of the Fresnel zone of the current remote radio signal of the remote radio device in the process of receiving and transmitting the remote radio signal between the remote radio device and the moving carrier in the area to be positioned;
the position optimization module is used for optimizing initialization parameters of a Kalman filter through a deep learning model, and carrying out Kalman optimal estimation based on first position information determined by the remote radio equipment and second position information determined by a strapdown inertial navigation system of the motion carrier by utilizing the Kalman filter after optimization so as to obtain positioning information of the motion carrier; the first location information is determined based on the initial location;
Wherein, the position determination module further comprises:
a depth position information determining unit for performing time synchronization according to a first signal receiving time at which the moving carrier receives the transmission signal of the radio signal transmitting device and a second signal receiving time at which the radio signal receiving device receives the moving carrier return signal, to determine depth position information of the moving carrier in the tunnel using the micro control unit based on a time synchronization result;
correspondingly, the position determining module comprises:
the channel state information determining unit is used for establishing a search space based on the depth position information, and matching channel state information corresponding to the Fresnel zone of the current remote radio signal in the search space with a preset information base so as to determine target channel state information of the moving carrier in the area to be positioned;
and the first initial position determining unit is used for determining the transverse position information of the moving carrier in the tunnel according to the target channel state information and determining the initial position of the moving carrier based on the depth position information and the transverse position information.
6. The in-tunnel locating device of claim 5, wherein the location determination module comprises:
And the remote radio communication sub-module is used for respectively communicating with the remote radio signal transmitting equipment and the remote radio signal receiving equipment by utilizing the moving carrier in the area to be positioned, determining the signal angle during communication by utilizing the directional antenna of the remote radio equipment, and determining the initial position of the moving carrier according to the signal angle and the channel state information of the current remote radio signal Fresnel area of the remote radio equipment.
7. An electronic device comprising a processor and a memory; wherein the memory is for storing a computer program to be loaded and executed by the processor to implement the in-tunnel localization method of any one of claims 1 to 4.
8. A computer readable storage medium for storing a computer program which, when executed by a processor, implements the in-tunnel localization method of any one of claims 1 to 4.
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Non-Patent Citations (1)
| Title |
|---|
| 基于雷达测距与角位置辅助的SINS空中对准方法;徐博;王连钊;吴雯昊;李盛新;段腾辉;;中国惯性技术学报(第05期);全文 * |
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