CN113568018A - Method and device for updating positioning information, electronic equipment and storage medium - Google Patents
Method and device for updating positioning information, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses a method and a device for updating positioning information, electronic equipment and a storage medium, wherein the method comprises the following steps: resolving RTK differential data and satellite positioning data at the current moment to obtain original RTK positioning data corresponding to the current moment; processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through the Kalman filtering model to obtain target RTK positioning data corresponding to the current moment; and updating the original RTK positioning data at the current moment according to the target RTK positioning data at the current moment. By implementing the embodiment of the application, the positioning accuracy of the positioning equipment under the semi-shielding environment can be improved under the condition of not introducing other hardware information sources, and the data fusion degree and the configuration cost of the positioning system are reduced.
Description
Technical Field
The present application relates to the field of positioning technologies, and in particular, to a method and an apparatus for updating positioning information, an electronic device, and a storage medium.
Background
An RTK (Real-Time Kinematic) carrier phase differential technique is a differential method for processing carrier phase observations of two measurement stations in Real Time, and transmits a carrier phase acquired by a reference station to a user receiver for calculating a difference and position and speed information. However, RTK is prone to positioning jump and drift in a semi-shielded complex environment.
The traditional solution is to perform multi-sensor data fusion between RTK positioning data and IMU (Inertial Measurement Unit, tubular Measurement Unit), vision sensor, laser radar, wheel-type odometer, etc. to improve positioning performance. However, the method does not improve the positioning accuracy of the RTK positioning, and the data fusion difficulty and the positioning system configuration cost are high.
Disclosure of Invention
The embodiment of the application discloses a method and a device for updating positioning information, electronic equipment and a storage medium, which can improve the positioning precision of RTK positioning and reduce the difficulty of data fusion and the configuration cost of a positioning system.
The embodiment of the application discloses a method for updating positioning information, which comprises the following steps: resolving RTK differential data and satellite positioning data at the current moment to obtain original RTK positioning data corresponding to the current moment; processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through a Kalman filtering model to obtain target RTK positioning data corresponding to the current moment; and updating the original RTK positioning data of the current moment according to the target RTK positioning data of the current moment.
The embodiment of the application discloses a positioning information's update device, the device includes: the resolving module is used for resolving the RTK differential data and the satellite positioning data at the current moment to obtain original RTK positioning data corresponding to the current moment; the positioning module is used for processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through a Kalman filtering model to obtain target RTK positioning data corresponding to the current moment; and the updating module is used for updating the original RTK positioning data of the current moment according to the target RTK positioning data of the current moment.
The embodiment of the application discloses a positioning device, which comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is enabled to realize any positioning information updating method disclosed by the embodiment of the application.
The embodiment of the application discloses a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to realize any one of the positioning information updating methods disclosed in the embodiment of the application.
Compared with the related art, the embodiment of the application has the following beneficial effects:
resolving the obtained RTK differential data and the satellite positioning data of the current moment to obtain original RTK positioning data of the current moment, processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the previous moment through a Kalman filtering model to obtain target RTK positioning data corresponding to the current moment, and updating the original RTK positioning data of the current moment by adopting the target RTK positioning data corresponding to the current moment. The RTK original data at the current moment and the target RTK positioning data at the previous moment are subjected to data fusion through a Kalman filtering model, the original RTK positioning data at the current moment are updated according to the obtained RTK target positioning data at the current moment, iteration updating is continuously carried out on the original RTK positioning data, the positioning accuracy of RTK positioning can be improved under the condition that other hardware information sources are not introduced, and the configuration cost of data fusion degree and a positioning system is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1A is a schematic view of an application scenario of a method for updating positioning information disclosed in an embodiment of the present application;
FIG. 1B is a schematic diagram of a positioning apparatus according to an embodiment of the disclosure;
fig. 2 is a schematic flowchart of a method for updating positioning information according to an embodiment of the present application;
fig. 3 is a diagram showing the effect of longitude improvement after the positioning information updating method of the present application is adopted;
fig. 4 is a diagram illustrating the effect of latitude improvement after the positioning information updating method of the present application is adopted;
FIG. 5 is a flowchart illustrating a process of acquiring target RTK positioning data corresponding to a current time according to an embodiment;
fig. 6 is a schematic structural diagram of an apparatus for updating positioning information disclosed in an embodiment of the present application;
fig. 7 is a schematic structural diagram of another apparatus for updating positioning information disclosed in an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the examples and figures of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
An RTK (Real-Time Kinematic) carrier phase differential technique is a differential method for processing carrier phase observations of two measurement stations in Real Time, and transmits a carrier phase acquired by a reference station to a positioning device for calculating a difference and position and speed information. However, in a semi-shielded load environment, due to the fact that the satellite positioning calculation accuracy is affected by reduction of visible satellite data or poor space geometric distribution, or due to the fact that a multipath effect is easily generated in the satellite signal propagation process, a large error is generated in RTK calculation, or due to the fact that a differential signal transmission link is unstable, differential data sent to positioning equipment by a reference station is delayed or interrupted, the RTK calculation accuracy is affected, and therefore positioning jumping points and drifting of RTK are easily generated in the semi-shielded complex environment.
The traditional solution is to perform multi-sensor data fusion between the positioning device and an IMU (Inertial Measurement Unit, tubular Measurement Unit), a vision sensor, a laser radar, a wheel-type odometer, and the like to improve the positioning performance. In this solution, the positioning accuracy of the RTK positioning itself is not improved, that is, the positioning effect when the RTK positioning method is used alone for positioning is not improved, and the difficulty of fusing the positioning data of the RTK positioning and the data of the at least one external sensor is high, for the positioning system, more parts and data processing parts with high performance need to be configured, which makes the configuration cost of the positioning system high.
The embodiment of the application discloses a method and a device for updating positioning information, positioning equipment and a storage medium, which can improve the display effect of a vehicle model. The following are detailed below.
Referring to fig. 1A, fig. 1A is a schematic view of an application scenario of a method for updating positioning information according to an embodiment of the present application. As shown in fig. 1A, a positioning apparatus 10 and a reference station 20 may be included, the positioning apparatus being fixed or movable within a range of the reference station. As shown in fig. 1, the reference station 20 may include a first reference station and a second reference station, and the positioning apparatus may receive RTK differential data at the current time transmitted by the first reference station and the second reference station, and may also automatically select the first reference station or the second reference station to receive the RTK differential data at the current time transmitted by the reference station.
Referring to fig. 1B, fig. 1B is a schematic structural diagram of a positioning apparatus according to an embodiment. As shown in FIG. 1B, the pointing device 10 includes a processor 110, an information collection module 120, and a wireless communication module 130.
In the embodiment of the present application, the processor 110 may include a Micro Controller Unit (MCU), a pointing device computer, and the like, but is not limited thereto. The processor 110 may be the control center of the pointing device 10, connect various portions of the overall pointing device using various interfaces and lines, and perform various functions of the pointing device and process data.
In this embodiment, the information collecting module 120 may include one or more satellite signal receiving devices, and the satellite signal receiving devices may be disposed at different positions of the positioning apparatus, which is not limited in particular. The information acquisition module 120 may acquire satellite signals of one or more satellites under the control of the processor 110.
In the embodiment of the present application, the wireless communication module 130 may receive the transmitted wireless signal of the reference station 20 through the antenna under the control of the processor 110. Among others, wireless signals may include, but are not limited to: ultra Wide Band (UWB) signals, Wi-Fi signals.
In one embodiment, the positioning device may be as shown in the aforementioned positioning device 10, and has information collection and wireless communication capabilities, when performing RTK positioning at the current time, the positioning device may receive RTK differential data transmitted by the reference station 20, and at the same time, the positioning device collects satellite positioning data at the current time, and resolves the received RTK differential data at the current time and the collected satellite positioning data at the current time to obtain original RTK positioning data at the current time, and then performs data fusion on the original RTK positioning data at the current time and target RTK positioning data at the previous time through a kalman filter model to obtain target RTK positioning data at the current time.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for updating positioning information according to an embodiment of the present disclosure, where the method is applicable to the aforementioned positioning apparatus. As shown in fig. 2, the method may include the steps of:
210. and resolving the RTK differential data and the satellite positioning data at the current moment to obtain the original RTK positioning data corresponding to the current moment.
In an embodiment of the present application, an RTK positioning system may include a positioning device 10, which may include, but is not limited to, a smart terminal, a positioner, a car, and the like, and a reference station 20. Both a positioning device and a reference station in the RTK positioning system perform power-on initialization, which may include starting circuit hardware, initializing relevant parameters of the RTK positioning system according to a preset value, and starting to search for a satellite. The reference station 20 may receive the carrier phase at the current time through the satellite receiver, wherein the RTK differential data includes at least coordinate information of the reference station setting position and the carrier phase at the current time received through the satellite receiver. The reference station may transmit RTK differential data at the current time to the positioning apparatus 10 in real time by wireless transmission, such as radio transmission. The positioning device 10 receives the carrier phases of the satellites through an information acquisition module 120 such as a satellite receiver. After the positioning device 10 receives the RTK differential data at the current time sent by the reference station 20 and the information acquisition module 120 acquires the carrier phase of the satellite at the current time, the original RTK positioning data corresponding to the current time of the positioning device 10 can be calculated in real time according to the principle of relative positioning. The raw RTK positioning data may be, among other things, the three-dimensional coordinates and the three-dimensional velocity of the positioning apparatus 10. The reference station 20 may be a fixed reference station. If the reference station is a fixed reference station, the fixed reference station needs to be pre-erected on a point with known three-dimensional coordinates.
In the embodiment of the present application, the RTK positioning system may include a GNSS positioning system capable of performing carrier phase difference operation and obtaining navigation information such as position and velocity, which is not limited in detail herein.
In one embodiment, the RTK differential data received by the positioning apparatus at the current time may be transmitted by the virtual reference station. Unlike the fixed reference stations, in the virtual reference station network, each reference station does not directly send any RTK differential data to the positioning device, but sends the RTK differential data of each reference station to the control center of the virtual reference station network through a data communication line. Meanwhile, before working, the positioning device sends a coordinate with lower precision of the position of the positioning device to the control center through a short message function of a Global System for Mobile Communications (GSM), and after receiving the position information corresponding to the approximate coordinate, the control center automatically selects one or more optimal reference stations by a computer according to the position information. And integrally correcting the orbit error of the GPS, the error caused by ionosphere, troposphere and atmospheric refraction according to the RTK differential data of the selected reference station, and sending the high-precision RTK differential data to the positioning equipment. The RTK differential data has the effect that a virtual reference station is generated beside the positioning equipment, so that the problem of limitation on the RTK working distance is effectively solved, and the precision of the positioning equipment is ensured.
220. And processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through the Kalman filtering model to obtain the target RTK positioning data corresponding to the current moment.
In this application embodiment, the positioning device may first obtain the target RTK positioning data obtained after the last time kalman filter model is processed, that is, the target RTK positioning data corresponding to the last time, and then process the original RTK positioning data corresponding to the current time and the RTK positioning data corresponding to the last time through the kalman filter model to obtain the target RTK positioning data corresponding to the current time. The Kalman filtering model is a model adopting a Kalman filtering algorithm, namely a prediction algorithm based on a probability statistics principle, and the model is used for removing noise in changed data to predict the future output of the system.
230. And updating the original RTK positioning data at the current moment according to the target RTK positioning data at the current moment.
In the embodiment of the application, the positioning device updates the original RTK positioning data corresponding to the current time according to the obtained target RTK positioning data corresponding to the current time. The updating mode can be that target RTK positioning data corresponding to the current moment is adopted to replace original RTK positioning data corresponding to the current moment; the state equation in the Kalman filtering model at the next moment can be constructed by adopting the target position information and the target speed information corresponding to the current moment.
In the embodiment of the present application, please refer to fig. 3, and fig. 3 is a diagram illustrating an effect of longitude improvement after the method for updating positioning information of the present application is adopted. The dotted line in fig. 3 represents a schematic diagram of longitude change in the target RTK positioning data obtained after the positioning information updating method of the present application is used, and the solid line in fig. 3 represents a schematic diagram of longitude change in the original RTK positioning data output by the RTK positioning, which shows that the accuracy of the positioning effect is higher after the positioning information updating method of the present application is used. Referring to fig. 4, fig. 4 is a diagram illustrating an effect of latitude improvement after the method for updating positioning information of the present application is adopted. The dotted line in fig. 4 represents a schematic diagram of a latitude change in the target RTK positioning data obtained after the positioning information updating method of the present application is used, and the solid line in fig. 4 represents a schematic diagram of a latitude change in the original RTK positioning data output by the RTK positioning method, which shows that the accuracy of the positioning effect is higher after the positioning information updating method of the present application is used.
In the embodiment of the application, the RTK differential data and the satellite positioning data of the current moment are obtained through resolving, the original RTK positioning data of the current moment are obtained, the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the previous moment are processed through the Kalman filtering model, the target RTK positioning data corresponding to the current moment are obtained, and the original RTK positioning data of the current moment are updated through the target RTK positioning data corresponding to the current moment. The RTK original data at the current moment and the target RTK positioning data at the previous moment are subjected to data fusion through a Kalman filtering model, the original RTK positioning data at the current moment are updated according to the obtained RTK target positioning data at the current moment, iteration updating is continuously carried out on the original RTK positioning data, the positioning accuracy of RTK positioning can be improved under the condition that other hardware information sources are not introduced, and the configuration cost of data fusion degree and a positioning system is reduced.
In one embodiment, the raw RTK positioning data may include raw position information and raw velocity information, and the target RTK positioning data includes target position information and target velocity information, where the raw position information and the target position information may each be in the form of three-dimensional coordinates, e.g., px, py, pz may represent the three-dimensional coordinates of the location of the positioning device, and the raw velocity information and the target velocity information may each be in the form of three-dimensional velocities, e.g., vx, vy, vz may represent the three-dimensional velocity of the positioning device.
Therefore, before the original RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filtering model are processed by the kalman filtering model to obtain the target RTK positioning data corresponding to the current time, the method further includes:
establishing an observation equation in a Kalman filtering model according to the original position information and the original speed information of the current moment;
and constructing a state equation in the Kalman filtering model according to the target position information at the previous moment and the target speed information at the previous moment.
In the embodiment of the present application, the observation equation in the constructed kalman filter model may be represented in a matrix form, and may specifically be represented by the following formula (1):
wherein, pxo、pyo、pzoThree-dimensional position, vx, in the raw RTK positioning data representing the current time instanto、vyo、vzoThe three-dimensional velocity in the original RTK positioning data representing the current time, the three-dimensional position and the three-dimensional velocity in the original RTK positioning data corresponding to the current time as described above, form a six-dimensional matrix of the original RTK positioning data corresponding to the current time, i.e., a current time Kalman filter modeObservation equation z in typek。
In the embodiment of the present application, the state equation in the constructed kalman filtering model may be represented in a matrix form, and may specifically be represented by the following formula (2):
wherein, pxk-1、pyk-1、pzk-1Representing the three-dimensional position, vx, in the target RTK positioning data corresponding to the last instant of timek-1、vyk-1、vzk-1The three-dimensional speed in the target RTK positioning data corresponding to the last moment is represented, and the three-dimensional position and the three-dimensional speed in the target RTK positioning data corresponding to the last moment form a six-dimensional matrix of the target RTK positioning data corresponding to the last moment, namely a state equation p in a Kalman filtering model at the current momentk-1。
In an embodiment, please refer to fig. 5, and fig. 5 is a flowchart illustrating a process of acquiring target RTK positioning data corresponding to a current time according to an embodiment. The original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model are processed through the Kalman filtering model, and the target RTK positioning data corresponding to the current moment are obtained, and the method comprises the following steps:
510. and performing state estimation on the current moment according to the target RTK positioning data corresponding to the previous moment through a Kalman filtering model to obtain a state estimation result.
520. And updating the state estimation result by taking the original RTK positioning data corresponding to the current moment as an observation value to obtain the target RTK positioning data corresponding to the current moment.
In the embodiment of the application, the positioning device extracts the target RTK positioning data obtained after processing by the last-time kalman filtering model, that is, the target RTK positioning data corresponding to the last time, and performs state estimation of the current time in the kalman filtering model according to the target RTK positioning data corresponding to the last time after extraction, so as to obtain a state estimation result. The state estimation result is the RTK positioning data corresponding to the current moment estimated according to the target RTK positioning data corresponding to the previous moment. The positioning device takes the original RTK positioning data corresponding to the current moment as an observed value in the Kalman filtering model, and updates the state estimation result of the current moment according to the observed value, so as to obtain the target RTK positioning data corresponding to the current moment. And estimating a state estimation result of the current moment according to the target RTK positioning data of the previous moment, and updating the state estimation result by using the acquired original RTK positioning data of the current moment as an observation value. By the method, the target RTK positioning data at the previous moment and the original RTK positioning data at the current moment can be reasonably fused, and compared with the ordinary method of directly adopting the original RTK positioning data or the state estimation result, the obtained RTK target positioning data at the current moment has higher accuracy.
In one embodiment, performing state estimation on the current time according to target RTK positioning data corresponding to the previous time through a kalman filtering model to obtain a state estimation result, includes:
and converting the target RTK positioning data corresponding to the previous moment through a state transition matrix in the Kalman filtering model to obtain a state estimation result of the current moment.
In the embodiment of the application, the positioning device converts the target RTK positioning data corresponding to the previous moment through a state transition matrix in a Kalman filtering model, and obtains a state estimation result of the current moment after conversion. The state transition matrix may be set according to a conversion relationship between the target RTK positioning data corresponding to the previous time and the state estimation result of the current time.
In the embodiment of the present application, according to the above state equation constructed according to the three-dimensional position and the three-dimensional velocity in the target RTK positioning data corresponding to the previous time, in combination with the state transition matrix, the conversion formula capable of obtaining the state estimation result at the current time is expressed as the following formula (3):
wherein the content of the first and second substances,
as a result of the state estimation at the current time,
the target RTK positioning data corresponding to the last time is obtained, and a is a transformation matrix.
In the embodiment of the present application, the above-mentioned conversion formula for obtaining the state estimation result at the current time, that is, the formula (3), may be expressed in a matrix form, and may specifically be expressed as the following formula (4):
wherein the content of the first and second substances,
representing the three-dimensional position in the state estimation result corresponding to the current time,
and representing the three-dimensional speed in the state estimation result corresponding to the estimated current time, and forming a six-dimensional matrix of the state estimation result corresponding to the current time by the three-dimensional position and the three-dimensional speed in the state estimation result corresponding to the current time. The state transition matrix is based on the conventional displacement formula x ═ x of uniform linear motion0+v0t and velocity formula v ═ v0The correlation between the displacement and the velocity according to which the state transition matrix is set is not limited to this. The state transition matrix is represented by the following formula (5):
where A is the state transition matrix and dt is the time interval between the set times.
In one embodiment, updating the state estimation result by using the original RTK positioning data corresponding to the current time as an observation value to obtain a target RTK positioning data corresponding to the current time includes:
and calculating the Kalman gain corresponding to the current moment according to the error covariance corresponding to the current moment, wherein the error covariance corresponding to the current moment is calculated according to the state transition matrix and the error covariance corresponding to the previous moment.
And updating the state estimation result according to the Kalman gain corresponding to the current time and the original RTK positioning data corresponding to the current time to obtain target RTK positioning data corresponding to the current time.
In the embodiment of the application, in the process of updating the state estimation result through the kalman filter model according to the original RTK positioning data corresponding to the current time, specifically, an error covariance corresponding to the current time needs to be calculated in advance, a kalman gain corresponding to the current time is calculated according to the error covariance corresponding to the current time, and then the state estimation result of the current time is updated according to the kalman gain corresponding to the current time and the original RTK positioning data corresponding to the current time, so as to obtain the target RTK positioning data corresponding to the current time. The error covariance corresponding to the current moment is obtained by calculation according to the state transition matrix and the error covariance corresponding to the previous moment, and the transition matrix required by the error covariance corresponding to the current moment is the same as the state transition matrix required by the state estimation result of the current moment.
In this embodiment, a calculation formula of the target RTK positioning data corresponding to the current time in a matrix form may be obtained according to the calculated error covariance corresponding to the current time, the kalman gain corresponding to the current time, the state estimation result of the current time, and the original RTK positioning data corresponding to the current time, where the formula may be specifically expressed as the following formula (6):
wherein the content of the first and second substances,
for the state estimation result at the current time, zkFor the original RTK positioning data, K, corresponding to the current timekFor the kalman gain at the current time, H is a measurement matrix, which may be H ═ I6×6。
In an embodiment, in the process of converting the error covariance corresponding to the previous time by using the conversion matrix to obtain the error covariance corresponding to the current time, the process noise variance also needs to be considered, so that in the process of obtaining the error covariance corresponding to the current time in the kalman filtering model, the process may be represented in a matrix form, specifically represented by the following formula (7):
wherein the content of the first and second substances,
is the error covariance matrix at the current time, A is the transformation matrix, Pk-1And Q is a process noise variance matrix. The accuracy of the error covariance at the current time obtained by calculation can be improved.
In an embodiment, in the process of calculating the kalman gain corresponding to the current time according to the error covariance corresponding to the current time, the measurement noise variance also needs to be considered, so that in the process of acquiring the kalman gain corresponding to the current time in the kalman filtering model, the process may be expressed in a matrix form, specifically expressed as the following equation (8):
wherein, KkFor the kalman gain at the present moment in time,
is an error covariance matrix at the current moment, H is a measurement matrix, and may be H ═ I6×6And R is a measurement noise equation matrix. The accuracy of the kalman gain at the present time can be improved.
In one embodiment, after updating the original RTK positioning data at the current time based on the target RTK positioning data at the current time, the method further comprises:
and updating the error covariance corresponding to the previous moment according to the error covariance corresponding to the current moment.
In the embodiment of the application, the positioning device obtains the error covariance corresponding to the current time through the kalman filter model, updates the state estimation result of the current time by using the error covariance corresponding to the current time, the kalman gain and the like, and needs to update the error covariance corresponding to the previous time according to the error covariance corresponding to the current time after obtaining the target RTK positioning data corresponding to the current time. Before updating the error covariance corresponding to the previous moment, the error covariance corresponding to the current moment can be updated by using the kalman filter gain, and then the error covariance corresponding to the current moment after updating can be updated.
In this embodiment of the present application, the process of updating the error covariance corresponding to the current time by using the kalman filter gain may be represented by using a matrix form, which may be specifically represented by the following formula (9):
wherein, PkThe method is an error covariance matrix corresponding to the current moment after Kalman filtering gain updating. After updating, the error covariance matrix P corresponding to the updated current time can be adoptedkReplacing the error covariance matrix P corresponding to the previous momentk-1To make the error of the previous timeAnd updating the covariance. The accuracy of the covariance can be effectively improved, and the accuracy of the updating of the positioning information in the subsequent circulation process is further improved.
In one embodiment, before the original RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filtering model are processed by the kalman filtering model to obtain the target RTK positioning data corresponding to the current time, the method further includes:
detecting a state factor at the current moment;
if the state factor is not larger than the state threshold, executing a step of processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through the Kalman filtering model to obtain target RTK positioning data corresponding to the current moment;
the method further comprises the following steps:
and if the state factor is larger than the state threshold, performing state estimation on the current moment through a Kalman filtering model according to the target RTK positioning data corresponding to the previous moment to obtain the target RTK positioning data corresponding to the current moment.
In the embodiment of the application, the positioning system outputs the state factor at each moment, and the state factor is used for evaluating the position state, the speed state, the longitude and latitude state and the like of the positioning system at the current moment. Illustratively, the state factors output by the positioning system at the current time include a position state factor, a speed state factor and a latitude and longitude state factor, which are respectively used for evaluating the position state, the speed state and the latitude and longitude state of the positioning system at the current time.
The positioning device detects the status factor output by the positioning system, and if the status factor is not greater than the status threshold, the positioning device performs step 220. The state threshold value can be set manually or according to empirical data. For example, the horizontal accuracy factor HDOP may be set according to the test experience, for example, if the comparison threshold is not greater than 0.8, the step 220 is executed.
In this embodiment of the present application, when the positioning device detects at least one status factor output by the positioning system, if the at least one status factor is not greater than the corresponding status threshold, the positioning device performs step 220; alternatively, if all the status factors are not greater than the corresponding status thresholds, the positioning apparatus performs the step 220.
In the embodiment of the application, the positioning device detects a state factor output by the positioning system, and if the state factor is greater than a state threshold, the positioning device performs state estimation on the current moment according to the target RTK positioning data corresponding to the previous moment through a kalman filter model to obtain the target RTK positioning data corresponding to the current moment. Namely, the positioning device performs state estimation on the current time according to the target RTK positioning data corresponding to the previous time through the kalman filter model, and after the state estimation result of the current time is obtained, the state estimation result of the current time does not need to be updated by using the original RTK positioning data corresponding to the current time, and the state estimation result of the current time is directly used as the target RTK positioning data corresponding to the current time.
In the embodiment of the present invention, before the step 220 is executed at each time, the state factor may be detected, and the subsequent operation may be determined according to the magnitude relationship between the state factor and the state threshold.
In one embodiment, after the step of updating the original RTK positioning data at the current time according to the target RTK positioning data at the current time is performed, the positioning device iteratively feeds back the target RTK positioning data at the current time to an updating process of positioning information at the next time, and the method includes:
resolving the RTK differential data and the satellite positioning data at the next moment to obtain original RTK positioning data corresponding to the next moment;
processing the original RTK positioning data corresponding to the next moment and the target RTK positioning data corresponding to the current moment obtained by the Kalman filtering model through the Kalman filtering model to obtain target RTK positioning data corresponding to the next moment;
and updating the original RTK positioning data of the next moment according to the target RTK positioning data of the next moment.
In this embodiment, the current time is a certain time in the positioning process of the positioning device, and is not limited to this. Correspondingly, the previous time and the next time are respectively the previous positioning time and the next positioning time relative to the current time. For example, the positioning device includes 10 times t0-t9 in the process of positioning, where the current time is t1, the previous time is t0, and the next time is t 2. And after the target RTK positioning data at the next moment is obtained in the updating process of the positioning information which is fed back to the next moment in an iterative manner, the target RTK positioning data at the next moment is fed back to the updating process of the positioning information at the next moment in an iterative manner, and the process is circulated until the positioning equipment stops positioning. In the above cycle process, the current time when the positioning information is updated is used as the current time.
In one embodiment, when the positioning device starts an updating process of the positioning information, that is, when there is no target RTK positioning data corresponding to the last time obtained by the kalman filter model but only original RTK positioning data corresponding to the initial time, an observation equation in the kalman filter model is constructed according to the original position information of the initial time and the original velocity information of the initial time, and a state equation in the kalman filter model is constructed according to the original position information of the initial time and the original velocity information of the initial time.
For example, the state equation at the initial time may be expressed as the following equation (10):
the observation equation at the initial time can be expressed as the following equation (11):
wherein, px0、py0、pz0Original RTK positioning number representing initial timeFrom the three-dimensional position in, vx0、vy0、vz0The three-dimensional velocity in the raw RTK positioning data representing the initial time, the three-dimensional position and the three-dimensional velocity in the raw RTK positioning data corresponding to the initial time, and the six-dimensional matrix of the raw RTK positioning data corresponding to the initial time, namely the observation equation z in the initial time Kalman filtering model0。
Representing the three-dimensional position in the state estimation result corresponding to the initial time,
the three-dimensional velocity in the state estimation result corresponding to the initial time, and the three-dimensional position and the three-dimensional velocity in the state estimation result corresponding to the initial time constitute a six-dimensional matrix of the state estimation result corresponding to the initial time.
Performing state estimation on the initial moment according to the original RTK positioning data corresponding to the initial moment through a Kalman filtering model to obtain a state estimation result; and updating the state estimation result by taking the original RTK positioning data corresponding to the initial moment as an observation value, and after obtaining the target RTK positioning data corresponding to the initial moment, iteratively feeding back the target RTK positioning data corresponding to the initial moment to the updating process of the positioning information at the next moment. Because the positioning result can be converged quickly, the precision of the positioning result is not greatly influenced, the introduction of information sources of other hardware components in the initial stage can be avoided, and the configuration cost of the positioning system is effectively reduced.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an updating apparatus of positioning information disclosed in an embodiment of the present application, where the updating apparatus of positioning information is applicable to a positioning device, such as a positioning device for driving a vehicle, and is not limited specifically. As shown in fig. 6, the vehicle model display apparatus 600 may include:
the resolving module 610 is configured to resolve the RTK differential data and the satellite positioning data at the current time to obtain original RTK positioning data corresponding to the current time;
the positioning module 620 is configured to process, through the kalman filter model, the original RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filter model to obtain target RTK positioning data corresponding to the current time;
an updating module 630, configured to update the original RTK positioning data at the current time according to the target RTK positioning data at the current time.
Resolving the obtained RTK differential data and the satellite positioning data of the current moment to obtain original RTK positioning data of the current moment, processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the previous moment through a Kalman filtering model to obtain target RTK positioning data corresponding to the current moment, and updating the original RTK positioning data of the current moment by adopting the target RTK positioning data corresponding to the current moment. The positioning effect of the positioning equipment can not be improved by performing data fusion on data of a plurality of sensors outside the positioning equipment and RTK original positioning data in a semi-shielding environment. Therefore, the RTK original data at the current moment and the target RTK positioning data at the previous moment are subjected to data fusion through the Kalman filtering model, the original RTK positioning data at the current moment are updated according to the obtained RTK target positioning data at the current moment, iteration updating is continuously carried out, the positioning accuracy of the positioning equipment under the semi-shielding environment can be improved under the condition that other hardware information sources are not introduced, and the data fusion degree and the configuration cost of a positioning system are reduced.
In an embodiment, the positioning module 620 may be further configured to perform state estimation on the current time according to target RTK positioning data corresponding to the previous time through a kalman filter model, so as to obtain a state estimation result;
and updating the state estimation result by taking the original RTK positioning data corresponding to the current moment as an observation value to obtain the target RTK positioning data corresponding to the current moment.
In an embodiment, the positioning module 620 is further configured to convert the target RTK positioning data corresponding to the previous time through the state transition matrix to obtain a state estimation result of the current time.
In an embodiment, the positioning module 620 is further configured to calculate a kalman gain corresponding to the current time according to an error covariance corresponding to the current time, where the error covariance corresponding to the current time is calculated according to the state transition matrix and the error covariance corresponding to the previous time;
and updating the state estimation result according to the Kalman gain corresponding to the current time and the original RTK positioning data corresponding to the current time to obtain target RTK positioning data corresponding to the current time.
In an embodiment, the positioning module 620 may be further configured to detect a state factor of the current time before the original RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filtering model are processed by the kalman filtering model to obtain the target RTK positioning data corresponding to the current time.
If the state factor is not greater than the state threshold, the positioning module 620 may be further configured to process the original RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filtering model through the kalman filtering model, so as to obtain the target RTK positioning data corresponding to the current time.
If the state factor is greater than the state threshold, the positioning module 620 may be further configured to perform state estimation on the current time according to the target RTK positioning data corresponding to the previous time through a kalman filter model, so as to obtain the target RTK positioning data corresponding to the current time.
In one embodiment, the updating module 630 may be further configured to update the error covariance at the previous time according to the error covariance at the current time after updating the original RTK positioning data at the current time according to the target RTK positioning data at the current time.
Referring to fig. 7, fig. 7 is a schematic structural diagram of another apparatus for updating positioning information according to an embodiment of the present disclosure. The apparatus for updating the positioning information shown in fig. 7 is optimized by the apparatus for updating the positioning information shown in fig. 6. Compared with the updating apparatus of the positioning information shown in fig. 6, the updating apparatus 600 of the positioning information shown in fig. 7 may further include:
the raw RTK positioning data may include raw position information and raw velocity information and the target RTK positioning data may include target position information and target velocity information.
A building module 640, configured to build an observation equation in the kalman filter model according to the original position information of the current time and the original velocity information of the current time before the original RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filter model are processed by the kalman filter model to obtain the target RTK positioning data corresponding to the current time; and constructing a state equation in the Kalman filtering model according to the target position information at the previous moment and the target speed information at the previous moment.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an embodiment, where the electronic device is applicable to driving a vehicle, and is not limited in this respect. As shown in fig. 8, the electronic device 800 may include:
a memory 810 storing executable program code;
a processor 820 coupled to the memory 810;
the processor 820 calls the executable program code stored in the memory 810 to execute any one of the methods for updating the positioning information disclosed in the embodiments of the present application.
It should be noted that the electronic device shown in fig. 8 may further include components, which are not shown, such as a power supply, an input key, a camera, a speaker, a screen, an RF circuit, a Wi-Fi module, a bluetooth module, and a sensor, which are not described in detail in this embodiment.
The embodiment of the application discloses a computer-readable storage medium, which stores a computer program, wherein the computer program enables a computer to execute any one of the positioning information updating methods disclosed in the embodiment of the application.
An embodiment of the present application discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute any one of the methods for updating positioning information disclosed in the embodiments of the present application.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art should also appreciate that the embodiments described in this specification are all alternative embodiments and that the acts and modules involved are not necessarily required for this application.
In various embodiments of the present application, it should be understood that the size of the serial number of each process described above does not mean that the execution sequence is necessarily sequential, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present application, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, may be embodied in the form of a software product, stored in a memory, including several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of the embodiments of the present application.
It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by hardware instructions of a program, and the program may be stored in a computer-readable storage medium, where the storage medium includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM), or other Memory, such as a magnetic disk, or a combination thereof, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
The above detailed description is provided for a method, an apparatus, a wireless headset and a storage medium for updating positioning information, which are disclosed in the embodiments of the present application, and the present application is described in detail with reference to specific embodiments. Meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for updating positioning information, the method comprising:
resolving RTK differential data and satellite positioning data at the current moment to obtain original RTK positioning data corresponding to the current moment;
processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through a Kalman filtering model to obtain target RTK positioning data corresponding to the current moment;
and updating the original RTK positioning data of the current moment according to the target RTK positioning data of the current moment.
2. The method of claim 1, wherein the processing, by a kalman filter model, the raw RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filter model to obtain the target RTK positioning data corresponding to the current time comprises:
performing state estimation on the current moment according to the target RTK positioning data corresponding to the previous moment through the Kalman filtering model to obtain a state estimation result;
and updating the state estimation result by taking the original RTK positioning data corresponding to the current moment as an observation value to obtain target RTK positioning data corresponding to the current moment.
3. The method according to claim 2, wherein the performing, by the kalman filter model, a state estimation on the current time according to the target RTK positioning data corresponding to the previous time to obtain a state estimation result includes:
and converting the target RTK positioning data corresponding to the previous moment through a state transition matrix in the Kalman filtering model to obtain a state estimation result of the current moment.
4. The method according to claim 3, wherein the updating the state estimation result with the original RTK positioning data corresponding to the current time as an observation value to obtain the target RTK positioning data corresponding to the current time comprises:
calculating a Kalman gain corresponding to the current moment according to an error covariance corresponding to the current moment, wherein the error covariance corresponding to the current moment is calculated according to the state transition matrix and the error covariance corresponding to the previous moment;
and updating the state estimation result according to the Kalman gain corresponding to the current time and the original RTK positioning data corresponding to the current time to obtain target RTK positioning data corresponding to the current time.
5. The method of claim 4, further comprising:
and updating the error covariance corresponding to the previous moment according to the error covariance corresponding to the current moment.
6. The method of claim 1, wherein the raw RTK positioning data includes raw position information and raw velocity information, and the target RTK positioning data includes target position information and target velocity information;
before the processing, by the kalman filter model, the original RTK positioning data corresponding to the current time and the target RTK positioning data corresponding to the last time obtained by the kalman filter model to obtain the target RTK positioning data corresponding to the current time, the method further includes:
establishing an observation equation in the Kalman filtering model according to the original position information at the current moment and the original speed information at the current moment;
and constructing a state equation in the Kalman filtering model according to the target position information at the last moment and the target speed information at the last moment.
7. The method according to any one of claims 1 to 6, further comprising:
detecting a state factor at the current moment;
if the state factor is not larger than the state threshold, executing the step of processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through the Kalman filtering model to obtain the target RTK positioning data corresponding to the current moment;
and if the state factor is larger than the state threshold, performing state estimation on the current moment according to the target RTK positioning data corresponding to the previous moment through the Kalman filtering model to obtain the target RTK positioning data corresponding to the current moment.
8. An apparatus for updating positioning information, the apparatus comprising:
the resolving module is used for resolving the RTK differential data and the satellite positioning data at the current moment to obtain original RTK positioning data corresponding to the current moment;
the positioning module is used for processing the original RTK positioning data corresponding to the current moment and the target RTK positioning data corresponding to the last moment obtained by the Kalman filtering model through a Kalman filtering model to obtain target RTK positioning data corresponding to the current moment;
and the updating module is used for updating the original RTK positioning data of the current moment according to the target RTK positioning data of the current moment.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, causes the processor to implement the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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Application publication date: 20211029 |