WO2020043019A1 - 定位数据的处理方法及处理装置、计算设备和存储介质 - Google Patents

定位数据的处理方法及处理装置、计算设备和存储介质 Download PDF

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Publication number
WO2020043019A1
WO2020043019A1 PCT/CN2019/102211 CN2019102211W WO2020043019A1 WO 2020043019 A1 WO2020043019 A1 WO 2020043019A1 CN 2019102211 W CN2019102211 W CN 2019102211W WO 2020043019 A1 WO2020043019 A1 WO 2020043019A1
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data
positioning data
time series
positioning
sequence
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PCT/CN2019/102211
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English (en)
French (fr)
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罗远浩
李炳国
李鸣
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腾讯科技（深圳）有限公司
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Priority to EP19856027.8A priority Critical patent/EP3748297A4/en
Publication of WO2020043019A1 publication Critical patent/WO2020043019A1/zh
Priority to US17/017,515 priority patent/US11796686B2/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • G01S19/37—Hardware or software details of the signal processing chain
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/50—Determining position whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks
    • 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
    • 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
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/40—Correcting position, velocity or attitude
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/14—Receivers specially adapted for specific applications

Definitions

  • the present application relates to the field of navigation technology, and in particular, to a method and device for processing positioning data, a computing device, and a storage medium.
  • positioning algorithms commonly used in the positioning field based on Global Positioning System include: least squares method, extended Kalman filter algorithm, and second-order extended Kalman filter algorithm.
  • the least squares method uses a linear fit, using the point with the smallest Euclidean distance as an estimate of the current location.
  • the Kalman filter algorithm first predicts the current position, speed, GPS receiver clock difference, etc. according to the state equation; then, based on this state prediction, the prior estimates and the satellite position and speed provided by the satellite ephemeris, Carl The Mann filter can predict the GPS receiver's pseudo-range and Doppler offset values for each satellite. The difference between these measured values and the actual measured values (observed values) of the receiver forms the measurement residual.
  • Karl The Mann filter obtains the correction amount of the system state estimation value and the optimal estimation value after correction by processing the measurement residuals.
  • the least square method is the most basic and simple positioning algorithm. However, in dynamic multipath and other situations, the positioning effect of the least square method is very unsatisfactory.
  • the Kalman filter algorithm iteratively calculates GPS positioning data generated according to time series. Although it solves the problem of the unsatisfactory positioning effect of the least square method in scenarios such as multipath effect, it uses the last best estimate as the current prediction. The basis of the prior estimate is calculated, so the accumulation of errors cannot be avoided. For the case where the GPS slowly deviates from the route, the existing Kalman filter algorithm has the problem that the GPS cannot drift slowly due to the accumulation of errors, and the positioning effect is not ideal.
  • Embodiments of the present application provide a method and device for processing positioning data, a computing device, and a storage medium.
  • the method for processing positioning data includes: acquiring a first positioning data sequence generated by a moving target in a time sequence; performing filtering processing on the first positioning data sequence according to a preset filtering algorithm to obtain a filtered data sequence, and The filtering data sequence is subjected to adsorption calculation and an adsorption data sequence is obtained.
  • the preset filtering algorithm is an algorithm obtained by modifying the Kalman filtering algorithm according to the adsorption data sequence; outputting the filtered data sequence to obtain the moving target A second positioning data sequence; displaying a position corresponding to the second positioning data in the second positioning data sequence.
  • the apparatus for processing positioning data includes: an acquiring unit configured to acquire a first positioning data sequence generated by a moving target in a time sequence; and a processing unit configured to filter the first positioning data sequence according to a preset filtering algorithm.
  • the computing device includes a memory and a processor.
  • the memory stores an executable program.
  • the processor executes the positioning data according to the foregoing embodiment. Approach.
  • the computer-readable storage medium stores an executable program.
  • the processor executes the method for processing positioning data according to the foregoing embodiment.
  • the first positioning data sequence is filtered according to a preset filtering algorithm to obtain a filtered data sequence, and the filtered data sequence is adsorbed.
  • the adsorption data sequence obtained by the adsorption calculation is used to modify the Kalman filter algorithm.
  • the filtered data sequence may be output to obtain a second positioning data sequence of the moving target, and the first The position corresponding to the second positioning data in the two positioning data sequences.
  • the output of the second positioning data sequence eliminates to a certain extent the adverse effects caused by the positioning error of the moving target, especially the accumulation of errors caused by the slow positioning offset, so that the second positioning data in the second positioning data sequence
  • the corresponding position accurately reflects the actual position of the moving target, thereby improving the accuracy of positioning and navigation, and increasing the user's satisfaction with positioning and navigation products, such as car navigation products and mobile terminals.
  • FIG. 1 is a schematic diagram of a hardware environment according to an embodiment of the present application.
  • FIG. 2 is a flowchart of a method for processing positioning data according to an embodiment of the present application.
  • FIG. 3 is a schematic block diagram of a positioning data processing device according to an embodiment of the present application.
  • FIG. 4 is another flowchart of a method for processing positioning data according to an embodiment of the present application.
  • FIG. 5 is another flowchart of a method for processing positioning data according to an embodiment of the present application.
  • FIG. 6 is another flowchart of a method for processing positioning data according to an embodiment of the present application.
  • FIG. 7 is a schematic block diagram of a terminal device according to an embodiment of the present application.
  • FIG. 8 is a schematic diagram of a trajectory of a first positioning data sequence according to an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a trajectory of a positioning data sequence output by the original Kalman algorithm after processing the first positioning data sequence of FIG. 8.
  • FIG. 10 is a schematic diagram of a trajectory of a second positioning data sequence output by processing the first positioning data sequence of FIG. 8 according to the positioning data processing method according to the embodiment of the present application.
  • Terminal 100 positioning data processing device 10, acquisition unit 12, processing unit 14, suction module 142, filtering module 144, output unit 16, initialization unit 18, positioning system 200, server 300, terminal device 400, memory 410, processor 420.
  • a method for processing positioning data may be applied to a hardware environment and / or a software environment composed of a positioning system 200, a server 300, and a terminal 100.
  • the terminal 100 may acquire positioning data from the positioning system 200.
  • the terminal 100 may be connected to the positioning system 200 and the server 300 through a wired or wireless network.
  • the above networks include, but are not limited to, a wide area network, a metropolitan area network, or a local area network.
  • the terminal 100 includes, but is not limited to, a PC, a mobile phone, a tablet computer, a vehicle terminal, etc. .
  • the vehicle terminal is, for example, a vehicle navigation device.
  • the method for processing positioning data in the embodiment of the present application may be executed by the server 300, the terminal 100, or both the server 300 and the terminal 100.
  • a client application software may be installed on the terminal 100 for common execution.
  • the client application software can be navigation software or map software.
  • the terminal 100 and the server 300 may also be referred to as computing devices.
  • a method for processing positioning data according to an embodiment of the present application includes:
  • Step S1 Obtain a first positioning data sequence generated by the moving target in time sequence
  • step S2 the first positioning data sequence is filtered and a filtered data sequence is obtained according to a preset filtering algorithm, and the filtered data sequence is subjected to adsorption calculation to obtain an adsorbed data sequence.
  • the preset filtering algorithm is to perform Kalman filtering according to the adsorbed data sequence. An algorithm resulting from a modified algorithm;
  • step S3 a filtered data sequence is output to obtain a second positioning data sequence of the moving target.
  • Step S4 displaying the position corresponding to the second positioning data in the second positioning data sequence.
  • the positioning data processing method in the embodiment of the present application can be applied to the positioning data processing device 10 in the embodiment of the present application, that is, the positioning data processing device 10 in the embodiment of the present application can use the positioning data in the embodiment of the present application.
  • the processing method processes the first positioning data sequence to obtain a second positioning data sequence of the moving target, and obtains more accurate positioning information of the moving target.
  • the positioning data processing device 10 may be an independent computing device having a positioning data processing function, such as a terminal device or a server, or may be a component installed in the computing device by running the computing device. And realize the processing function of its positioning data.
  • the independent terminal device may be a car navigation device or another device having a navigation function.
  • the positioning data processing device 10 includes: an obtaining unit 12, a processing unit 14, and an output unit 16.
  • the obtaining unit 12 is configured to obtain a first positioning data sequence generated by the moving target in a time sequence.
  • the processing unit 14 is configured to perform filtering processing on the first positioning data sequence and obtain a filtered data sequence according to a preset filtering algorithm, and perform adsorption calculation on the filtered data sequence to obtain an adsorption data sequence.
  • the output unit 16 is configured to output a filtered data sequence to obtain a second positioning data sequence of the moving target.
  • the obtaining unit 12 may implement step S1 to locate the moving target by GPS during the movement of the moving target, that is, the obtaining unit 12 may send a positioning instruction to a GPS positioning system (such as the positioning system 200) and receive the GPS.
  • the positioning data returned by the positioning system is to obtain a first positioning data sequence generated by the moving target in a time sequence, and send the first positioning data sequence to the processing unit 14.
  • the first positioning data sequence includes a plurality of time-series first positioning data.
  • the first positioning data includes data such as a moving speed, a deflection angle, first position data (for example, longitude and latitude) of the moving target, and positioning accuracy.
  • the processing unit 14 may implement step S2 to the first positioning data sequence acquired by the acquisition unit 12 Perform filtering processing to reduce noise in the first positioning data.
  • the processing unit 14 performs filtering processing on the first positioning data in the first positioning data sequence generated in time sequence one by one to obtain the filtered data sequence.
  • the processing unit 14 may perform adsorption calculation on the filtered data sequence, and use the adsorption data sequence obtained by the adsorption calculation to modify the Kalman filtering algorithm, so that during the recursive calculation process in time series, The noise is reduced and the filtered data is sent to the output unit 16.
  • the output unit 16 may implement step S3 to output a filtered data sequence to obtain a second positioning data sequence of the moving target. Further, the output unit 16 outputs the second positioning data sequence to a display device of the terminal device, and the display device displays a position corresponding to the second positioning data in the second positioning data sequence. In the embodiment of the present application, the display device may also be a part of the output unit 16.
  • the step of displaying the position corresponding to the second positioning data may be performed by the display device after being triggered after receiving the second positioning data sequence, or may be The display device is executed after receiving a command input by a user through an input device such as a key or a touch screen.
  • the output of the second positioning data sequence eliminates to a certain extent the adverse effects caused by the positioning error of the moving target, especially the accumulation of errors caused by the slow positioning offset, so that the second positioning data in the second positioning data sequence
  • the corresponding position accurately reflects the actual position of the moving target, thereby improving the accuracy of positioning and navigation, and increasing the user's satisfaction with positioning and navigation products, such as car navigation equipment and mobile terminals.
  • the first positioning data sequence may be an original positioning data sequence, that is, an original positioning data sequence obtained by a GPS positioning system, and the first positioning data may be original positioning data.
  • the second positioning data sequence may be a positioning data sequence obtained by filtering the first positioning data sequence through a preset filtering algorithm, and the second positioning data may be obtained by filtering the first positioning data through a preset filtering algorithm. Positioning data.
  • Kalman filtering algorithm is a linear system state equation. It is an algorithm that inputs the observation data, processes the observation data, and outputs the processing results to achieve the best estimation of the system. Since the observation data includes the noise in the system, the best estimation is the process of filtering the noise signal in the system.
  • the prediction calculation is performed according to the best estimated value obtained in the previous round of filtering processing to obtain the predicted prior estimated value of the current filtering processing.
  • the observation data obtained by the current filtering process and the predicted prior estimate of the current filtering process can be used to jointly calculate the optimal estimated value after filtering.
  • the best estimated value is the filtering result of the positioning data, so that the noise of the positioning data after filtering is reduced.
  • the process of filtering the first positioning data by using a Kalman filter algorithm is to perform an iterative calculation on the first positioning data.
  • the Kalman filtering algorithm is modified according to the adsorption data sequence, so that the error accumulation due to the slow GPS drift during the process of filtering the first positioning data sequence by the Kalman filtering algorithm is further reduced. It is small, and the second positioning data sequence obtained is more accurate, which is conducive to improving positioning accuracy.
  • a part of the functions of the positioning data processing device 10 may be implemented by the server 300 or the terminal 100, another part of the functions may be implemented by the terminal 100 or the server 300, or another part of the functions may be implemented by a device independent of the server 300 and the terminal 100. All the functions of the positioning data processing device 10 may also be realized by the server 300 or the terminal 100 alone, or by devices independent of the server 300 and the terminal 100.
  • x k F k x k-1 + B k u k + w k
  • x k is the best estimated value of the k-th time series of the system state
  • x k-1 is the best estimated value of the k-th time series of the system state
  • F k is the state transition model
  • B k is the k-th series Time series system control input
  • u k is the system control vector of the k-th time series
  • w k is the process noise
  • z k is the observed value of the real state x k of the system state in the k-th real time space
  • H k is the observation model
  • v k is the observation noise.
  • the first positioning data sequence includes a first position data sequence
  • step S2 includes:
  • the first position data sequence of the moving target may be filtered, for example, the longitude and latitude of the moving target may be filtered to reduce the noise of the first position data of the moving target.
  • the processing unit 14 may be configured to perform filtering processing on the first position data sequence and obtain a filtered data sequence according to a preset filtering algorithm, perform adsorption calculation on the filtered data sequence, and obtain an adsorption data sequence.
  • the first position data of the moving target may be filtered, and the filtered data includes the filtered position data, the best estimated value of the k-th time series x k and the k-1th time series.
  • the best estimated value x k-1 is the filtered position data of the k-th time series and the filtered position data of the k-1 time series, respectively.
  • the first position data and the filtered position data may be the longitude of the moving target. And latitude, in the form of two-dimensional matrix in Kalman filter algorithm for filtering calculation.
  • the state transition model H k acts on the best estimated value x k-1 of the position data of the moving target at the k-1th time series.
  • the adsorption data obtained from the k-1th time-series adsorption calculation and the displacement calculated from the moving speed and deflection angle of the moving target at the k-1th time-series can be used as the system control input B k to act on the control vector u k , that is, That is, in the embodiment of the present application, the Kalman model and the equation are modified based on the adsorption data. Specifically, the Kalman model and equations at the k-th time series are modified according to the adsorption data calculated at the k-1 time series, and then the first positioning data at the k-th time series is filtered.
  • the process noise w k has a normal distribution and the mean value is 0, and the error covariance of the process noise is Q k , that is, w k ⁇ N (0, Q k ).
  • the error covariance of the process noise is the uncertainty of the process noise.
  • the observation value z k of the position data of the moving target can be considered as the actual value x k of the position data of the moving target in the real state space, which is obtained by mapping the observation model H k to the observation space.
  • the observation noise v k has a normal distribution and the mean value is 0, and the error covariance of the observation noise is R k , that is, v k ⁇ N (0, R k ).
  • the error covariance of the observation noise is the uncertainty of the observation noise.
  • the first time-series filtered data can be obtained by using the first time-series first positioning data
  • the first time-series adsorption data can be obtained by using the first time-series filtered data. Calculated.
  • the positioning data processing device 10 includes an initialization unit 18, and the processing unit 14 includes an adsorption module 142.
  • the initialization unit 18 may be configured to obtain the first time sequence by using the first positioning data of the first time sequence. Filtered data.
  • the first timing is a timing corresponding to the first positioning data in the positioning data sequence acquired by the acquiring unit 12 according to the timing.
  • the adsorption module 142 may be used to perform adsorption calculation using the filtered data of the first time series to obtain the adsorption data of the first time series.
  • the filtered data includes a best estimate and an error covariance of the best estimate.
  • the second positioning data sequence can be obtained by sequentially outputting the best estimated value of each time sequence.
  • the initialization unit 18 may be configured to use the first positioning data of the first time series to obtain the error covariance of the best estimated value of the first time series and the best estimated value of the first time series. . Further, the output unit 16 may output the second positioning data at the first timing with the best estimated value at the first timing.
  • the adsorption module 142 may be configured to perform adsorption calculation using the best estimated value of the first time sequence to obtain the first time adsorption data.
  • the processing unit 14 may use the filtered data of the first time sequence for the filtering processing of the first positioning data of the second time sequence.
  • the processing unit 14 uses the adsorption data to modify the Kalman filtering algorithm.
  • the initialization unit 18 may Use the first positioning data of the first time series to obtain the error covariance between the best estimated value of the first time series and the best estimated value of the first time series, that is, set the first one according to the first positioning data of the first time series The error covariance of the best estimate of the time series and the best estimate of the first time series.
  • the best estimated value of the first timing is set as:
  • m and n are the longitude and latitude of the first time sequence of the moving target, respectively.
  • the error covariance of the best estimate of the first time series is set to:
  • p is the positioning accuracy of the first time-series GPS positioning data of the moving target.
  • the initialization unit 18 is configured to initialize the error covariance of the state transition model, the observation model, and the process noise according to empirical values and statistical values.
  • the first positioning data of each time series will be calculated by using the error covariance of the state transition model, observation model, and process noise, and the error coherence of the state transition model, observation model, and process noise.
  • the variances remain unchanged during the recursive calculation.
  • the error covariances of the state transition model, the observation model, and the process noise can be initialized by the initialization unit 18 to determine their values, which is convenient for the processing unit 14 to locate the first location.
  • the data is filtered.
  • the state transition model is initialized as:
  • the observation model is initialized as:
  • the error covariance of the process noise is initialized as:
  • the error covariance of the state transition model, the observation model, and the process noise is determined according to empirical values and statistical values.
  • the initialization results of the error covariance of the state transition model, the observation model, and the process noise are only examples, and cannot be interpreted as an application to the present application.
  • the values of the error covariance of the state transition model, the observation model, and the process noise may be transformed according to actual needs, and are not specifically limited here.
  • the filtering data of the k-th time series may use the first positioning data of the k-th time series, the first positioning data of the k-1 time series, and the k-1
  • the filtered data of the first time series and the adsorption data of the k-1st time series are calculated by the Kalman filtering algorithm.
  • the adsorption data of the kth time series can be obtained by performing the adsorption calculation by using the filtered data of the kth time series.
  • the processing unit 14 includes a filtering module 144, and the filtering module 144 is configured to use the first positioning data of the k-th time series, the first positioning data of the k-1 time series, and the k-1
  • the filtered data of the first time series and the adsorption data of the k-1st time series are calculated by the Kalman filter algorithm to obtain the filtered data of the kth time series.
  • the adsorption module 142 is configured to perform adsorption calculation by using the filtered data of the k-th time sequence to obtain adsorption data of the k-th time sequence.
  • the processing unit 14 uses the adsorption data to modify the Kalman filter algorithm.
  • the filtering data of the second time series may be calculated by the filtering module 144 by using the first positioning data of the second time series, the filtering data of the first time series, and the adsorption data of the first time series by a Kalman filtering algorithm.
  • the adsorption data of the second time series may be obtained by the adsorption module 142 performing the adsorption calculation using the filtered data of the second time series.
  • the output unit 16 may output the second positioning data with the best estimated value of the k-th timing. In this way, the output unit 16 sequentially outputs the best estimated value of each time sequence of the filtered data sequence in order to obtain the second positioning data of the moving target.
  • the filtered data includes the predicted prior estimate, the error covariance of the predicted prior estimate, and the Kalman gain.
  • the predicted prior estimate of the k-th time series may use the k- The first position data of 1 time series, the best estimated value of the k-1 time series, and the adsorption data of the k-1 time series are calculated,
  • the error covariance of the predicted prior estimate of the k-th time series can be calculated by using the error covariance of the best estimate of the k-1 time series.
  • the filtering module 144 may be used to use the first position data of the k-1th time series, the best estimated value of the k-1th time series, and the adsorption data of the k-1th time series.
  • the predicted prior estimate of the k-th time series is obtained through the above-mentioned Kalman model and equation calculation, that is, the processing unit 14 uses the adsorption data of the k-1 time series to modify the Kalman filter algorithm to correct the k-th time series.
  • the first position data is filtered.
  • the filtering module 144 may be used to use the error covariance of the best estimated value of the k-1th time series and calculate the error covariance of the predicted prior estimated value of the kth time series by the above-mentioned Kalman model and equation calculation to evaluate the first Uncertainty of k prioritized estimates of time series.
  • the predicted prior estimate of the k-th time series is calculated according to the following equation:
  • Is the predicted prior estimate of the k-th time series of the moving target Is the best estimate of the k-1th time series of the moving target.
  • the moving speed and deflection angle of the moving target in the first positioning data of the k-1th time series and the adsorption data of the k-1 time series can be used as the system control input B k to act on the control vector u k , that is, the adsorption data is used for Kalman
  • the filtering algorithm is modified. In the calculation process, it is assumed that the value of the process noise is 0, and the error covariance of the process noise is Q k .
  • k-1 is the predicted prior estimate of the k-th time series of the moving target Error covariance
  • k-1 is the best estimate of the k-1 time series of the moving target Error covariance, where the error covariance of the process noise is:
  • the filtering module 144 is configured to obtain the error covariance of the predicted prior estimation value of the k-th time sequence and the predicted prior estimation value of the k-th time sequence by the above equation.
  • the filtered data includes residuals and error covariances of the residuals
  • the residuals of the k-th time series may use the first positioning data of the k-th time series and the predicted prior estimates of the k-th series and Calculated through observation model.
  • the error covariance of the residual at the k-th time series can be calculated by using the error covariance of the first positioning data at the k-th time series and the predicted prior estimation value of the k-th time series through an observation model.
  • the filtering module 144 may be configured to use the first positioning data of the k-th time series and the predicted prior estimation value of the k-th time series and calculate the residual of the k-th time series through an observation model. .
  • the filtering module 144 may be used to use the error covariance of the first positioning data of the k-th time series and the predicted prior estimation value of the k-th time series and calculate the error covariance of the residual of the k-th time series through an observation model.
  • the residual of the k-th time series can be calculated by the following equation:
  • the observation model H k acts on the predicted prior estimates of the k-th time series And the predicted prior estimate of the k-th time series Map to the same observation space as the observation value z k to facilitate the calculation of the residual at the k-th time series.
  • the observation noise is 0 and the error covariance of the observation noise is R k .
  • the observation value z k is the first position data of the k-th time series of the moving target in the GPS positioning data, that is,
  • m k and n k are the longitude and latitude of the k-th time series of the moving target, respectively.
  • Sk is the residual of the position data of the moving target at the k-th time series Error covariance.
  • the error covariance R k of the observation noise at the k-th time series is determined according to the positioning accuracy of the first positioning data at the k-th time series of GPS, that is,
  • p k is the positioning accuracy of the GPS positioning data at the k-th time series.
  • the Kalman gain of the k-th time series may use the error of the first position data of the k-th time series, the predicted a priori estimated value of the k-th time series, and the predicted a priori estimated value of the k-th time series. Covariance is calculated. Further, the Kalman gain of the k-th time series can be calculated by using an error covariance of the predicted prior estimation value of the k-th time series and the error covariance of the residual of the k-th time series.
  • the filtering module 144 may be used to use the error of the first position data of the k-th time series, the predicted prior estimate of the k-th time series, and the predicted prior estimate of the k-th time series.
  • the covariance is calculated by the above Kalman model and equations and the observation model to obtain the Kalman gain of the k-th time series.
  • the filtering module 144 may be used to use the error covariance of the predicted prior estimation value of the k-th time series and the error covariance of the residual of the k-th time series and calculate the Kalman gain of the k-time series by the observation model. .
  • the Kalman gain of the k-th timing can be calculated by the following equation:
  • K k is the Kalman gain of the position data of the moving target at the k-th timing.
  • the best estimated value of the k-th time series may be calculated by using the first position data of the k-th time series, the predicted prior estimate of the k-th time series, and the Kalman gain of the k-th time series. Further, the best estimated value of the k-th time sequence can be calculated by using the predicted prior estimated value of the k-th time sequence, the Kalman gain of the k-th time sequence, and the residual of the k-th time sequence.
  • the error covariance of the best estimate at the k-th time series can be calculated by using the error covariance of the predicted prior estimate at the k-th time series and the Kalman gain of the k-th series.
  • the filtering module 144 may be configured to calculate the k-th using the first position data of the k-th timing, the predicted prior estimate of the k-th timing, and the Kalman gain of the k-th timing. Best estimate of time series.
  • the output unit 16 may output the best estimated value of the k-th time sequence as the second positioning data of the k-th time sequence. And the best estimated value of the k-th time series can be used for the filtering processing of the first positioning data of the subsequent time series.
  • the filtering module 144 may be configured to calculate the error covariance of the best estimated value of the k-th time sequence by using the error covariance of the predicted prior estimate value of the k-th time sequence and the Kalman gain of the k-time sequence.
  • the error covariance of the best estimated value at the k-th time series can be used for filtering processing of the first positioning data at subsequent time series.
  • the best estimate of the k-th time series can be calculated by the following equation:
  • the best estimated value obtained can be regarded as the second position data of the k-th time sequence of the moving target, so that the output unit 16 can convert The best estimate is output as the second positioning data.
  • the j first positioning data generated at the j timings can be filtered by the output unit 16 to obtain j second positioning data at the j timings after filtering, that is, the output obtains the second moving target's second positioning data. Positioning data sequence.
  • the processing unit 14 may determine the error covariance of the best estimated value of the first time series and the error covariance of the observation noise according to the positioning accuracy of the GPS positioning data, and then according to the best estimate of the first time series
  • the error covariance of the values is calculated recursively to calculate the error covariance of the predicted prior estimates of subsequent time series, and to calculate the best estimated value of the corresponding time series based on the observed noise of each time series and the error covariance of the predicted prior estimates of each time series. Error covariance.
  • the first positioning data includes at least one of positioning accuracy, positioning signal strength, and number of positioning satellites
  • each timing can be calculated according to at least one of positioning accuracy, positioning signal strength, and number of positioning satellites.
  • the processing unit 14 may also use the positioning signal strength or the number of positioning satellites to determine the error covariance of the best estimate of the first timing in the first positioning data sequence and the error covariance of the observation noise, or use positioning accuracy Two or more combinations of the positioning signal strength and the number of positioning satellites determine the error covariance of the best estimate of the first timing of the first positioning data sequence and the error covariance of the observation noise. Further, the error covariance of the best estimated value of the first time series is calculated recursively based on the error covariance of the predicted prior estimated value of the subsequent time series, and the error of the predicted prior estimated value of each time series according to the observation noise of each time series Covariance calculates the error covariance of the best estimate of the corresponding time series.
  • the positioning data processing device 10 can generally set a planned route for a moving target before navigation. Therefore, the adsorption module 142 can be used to determine whether the position corresponding to the second positioning data of the kth time series deviates. Plan a route to navigate the moving target so that the moving target moves along the planned route.
  • the adsorption module 142 may be used to determine whether the position corresponding to the second positioning data of the first time sequence deviates from the planned route.
  • the adsorption data of the first time sequence can be obtained by performing the adsorption calculation using the filtered data of the first time sequence when the position corresponding to the second positioning data of the first time sequence does not deviate from the planned route.
  • the adsorption module 142 is configured to use the filtering data of the first time series to perform the adsorption calculation when the position corresponding to the second positioning data of the first time series does not deviate from the planned route. Time series of adsorption data.
  • the obtaining unit 12 obtains a positioning data sequence generated by the moving target according to a time sequence.
  • a planned route can be set in advance. After the navigation is started, it is determined whether the position corresponding to the second positioning data of the first time sequence deviates from the planned route. In this way, when the navigation is turned on, the position of the first sequence of the moving target deviates from the planned route, which can cause navigation errors.
  • the planned route is updated according to the filtered data of the first time sequence.
  • the adsorption module 142 is configured to update the planned route according to the filtered data of the first time sequence when the position corresponding to the second positioning data of the first time sequence does not deviate from the planned route.
  • the adsorption data at the k-th time sequence may be obtained by performing adsorption calculation by using the filtering data at the k-th time sequence when the position corresponding to the second positioning data at the k-th time sequence does not deviate from the planned route.
  • the adsorption module 142 is configured to perform the adsorption calculation using the filtered data of the k-th time series to obtain the k-th number when the position corresponding to the second positioning data of the k-th time series does not deviate from the planned route. Time series of adsorption data.
  • the position corresponding to the second positioning data of the k-th time sequence does not deviate from the planned route, it can be considered that the position of the k-th timing moving target is on the planned route.
  • the second positioning data of the k-th time sequence is being planned.
  • Corresponding position of the route get the k-th time-series adsorption data.
  • the adsorption data at the k-th time series can be used as a control input for calculating the predicted prior estimates of the subsequent time series, that is, used to modify the Kalman filter algorithm to filter the first positioning data at the subsequent time series.
  • the adsorption data at the k-th time series may be obtained by performing adsorption calculations using the best estimated value at the k-th time series.
  • the adsorption module 142 may be configured to perform adsorption calculation using the best estimated value of the k-th time series to obtain adsorption data of the k-th time series.
  • the best estimated value of the k-th time series is output by the output unit 16 to obtain the second positioning data of the k-th time series.
  • the adsorption data of the k-th time series may use the second position data of the k-th time series.
  • the positioning data is obtained by adsorption calculation.
  • the adsorption module 142 is configured to perform adsorption calculation by using the second positioning data of the k-th time sequence to obtain adsorption data of the k-th time sequence.
  • the corresponding position on the planned route of the k-th time-series second positioning data can be obtained through adsorption calculation.
  • the planned route is updated according to the second positioning data of the k-th time sequence.
  • the adsorption module 142 may be configured to correspond to the second positioning data of the k-th time series when the position corresponding to the second positioning data of the k-th time series of the moving target deviates from the planned route.
  • Location update planning route In this way, when the moving target deviates from the planned route, the planned route can be updated in real time to ensure that the moving target can reach the target position during the navigation process.
  • the position corresponding to the second positioning data is used as a starting point, and the planned route is newly determined.
  • the second positioning data of multiple consecutive time series is collected for judgment. If the positions corresponding to the second positioning data of the multiple time series deviate from the planned route, , It can be considered that the moving target deviates from the planned route, and then the planned route is updated according to the position corresponding to the second positioning data of the k-th time series.
  • a terminal device 400 includes a memory 410 and a processor 420.
  • the memory 410 stores an executable program.
  • the processor 420 executes any of the foregoing embodiments. Processing method of positioning data.
  • the processor 420 may perform the following steps:
  • Step S1 Obtain a first positioning data sequence generated by the moving target in time sequence
  • step S2 the first positioning data sequence is filtered and a filtered data sequence is obtained according to a preset filtering algorithm, and the filtered data sequence is subjected to adsorption calculation to obtain an adsorbed data sequence.
  • the preset filtering algorithm is to perform Kalman filtering according to the adsorbed data sequence. An algorithm resulting from a modified algorithm;
  • step S3 a filtered data sequence is output to obtain a second positioning data sequence of the moving target.
  • Step S4 displaying the position corresponding to the second positioning data in the second positioning data sequence.
  • the processor 420 may further perform the following steps: displaying a position corresponding to the second positioning data in the second positioning data sequence.
  • the processor 420 executes the positioning data processing method according to any one of the foregoing embodiments, and is used to implement positioning and navigation of a moving target.
  • the adsorption data sequence is used to modify the Kalman filter algorithm, and the calculation is recursively performed in time series.
  • the second positioning data sequence of the moving target can be obtained, so as to eliminate the moving target positioning offset to a certain extent, especially the effect of the accumulation of errors caused by the slow positioning offset, which makes the second positioning data sequence
  • the position corresponding to the second positioning data accurately reflects the actual position of the moving target, thereby improving the accuracy of positioning and navigation and improving the user's satisfaction with the terminal device 400.
  • the terminal device 400 may be the terminal 100 described above.
  • the terminal device 400 may communicate with the positioning system 200 and the server 300 to form a navigation system.
  • the above-mentioned positioning data processing device 10 may be provided in the terminal device 400.
  • the positioning data processing device 10 may be an independent functional component of the terminal device 400, or the positioning data processing device 10 Part of the functional modules may be implemented by the processor 420, or each functional module of the positioning data processing device 10 is implemented by the processor 420.
  • the method for processing positioning data is implemented by the terminal device 400.
  • the method may also be implemented by a server or other independent device.
  • the server or other independent device may have the same configuration as that shown in FIG. 7.
  • the similar structure shown includes a memory and a processor.
  • the memory stores an executable program.
  • the processor executes the positioning data processing method according to any one of the foregoing embodiments.
  • the computer-readable storage medium of the embodiment of the present application stores an executable program.
  • the processor 420 executes the positioning data processing method of any one of the foregoing embodiments.
  • the processor 420 may perform the following steps:
  • Step S1 Obtain a first positioning data sequence generated by the moving target in time sequence
  • step S2 the first positioning data sequence is filtered and a filtered data sequence is obtained according to a preset filtering algorithm, and the filtered data sequence is subjected to adsorption calculation to obtain an adsorbed data sequence.
  • the preset filtering algorithm is to perform Kalman filtering according to the adsorbed data sequence. An algorithm resulting from a modified algorithm;
  • step S3 a filtered data sequence is output to obtain a second positioning data sequence of the moving target.
  • Step S4 displaying the position corresponding to the second positioning data in the second positioning data sequence.
  • the processor 420 may further perform the following steps: displaying a position corresponding to the second positioning data in the second positioning data sequence.
  • the diagram is a partial trajectory diagram displayed by the terminal device, including a trajectory of a road and a trajectory of positioning data, where each point in the figure represents a position corresponding to the positioning data at each time .
  • Each point shown in FIG. 8 represents a trajectory of the first positioning data sequence of each time sequence, and each point in the trajectory corresponds to the first positioning data of a time sequence of the moving target.
  • Each point shown in FIG. 9 represents the trajectory of the positioning data sequence obtained by filtering the first positioning data sequence shown in FIG. 8 using the original Kalman filtering algorithm.
  • Each point shown in FIG. 10 represents a trajectory of the second positioning data sequence after the first positioning data sequence described in FIG. 8 is processed by using the positioning data processing method according to the embodiment of the present application.
  • the position corresponding to the first positioning data may be a situation where the moving target is located outside the road.
  • the vehicle usually travels along the road, that is, as shown in FIG. 8. There is noise in the first positioning data shown.
  • each point in the trajectory of the illustrated positioning data sequence corresponds to positioning data obtained by filtering the first positioning data of a moving target in a time sequence after filtering.
  • the position corresponding to the positioning data obtained after filtering processing using the original Kalman filter algorithm still exists outside the road. Therefore, the original Kalman filter algorithm cannot well eliminate the noise in the first positioning data.
  • each point in the trajectory of the second positioning data sequence corresponds to a time-series second positioning data of the moving target.
  • the trajectory and the road in the figure can coincide with each other, indicating noise in the second positioning data Smaller, that is, the positioning data processing method according to the embodiment of the present application can effectively eliminate noise in the positioning data.
  • the trajectory of the second positioning data sequence after the filtering process of the moving target conforms to the road trajectory.
  • the position corresponding to the second positioning data can accurately reflect the actual position of the moving target, making positioning and navigation more accurate and reliable.
  • Any process or method description in a flowchart or otherwise described herein can be understood as a module, fragment, or portion of code that includes one or more executable instructions for implementing a particular logical function or step of a process
  • the scope of the preferred embodiments of this application includes additional implementations in which the functions may be performed out of the order shown or discussed, including performing the functions in a substantially simultaneous manner or in the reverse order according to the functions involved, which should It is understood by those skilled in the art to which the embodiments of the present application pertain.
  • Logic and / or steps represented in a flowchart or otherwise described herein, for example, a sequenced list of executable instructions that may be considered to implement a logical function, may be embodied in any computer-readable medium, For use by, or in combination with, an instruction execution system, device, or device (such as a computer-based system, a system that includes a processor, or another system that can fetch and execute instructions from an instruction execution system, device, or device) Or equipment.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.
  • computer-readable media include the following: electrical connections (electronic devices) with one or more wirings, portable computer disk cartridges (magnetic devices), random access memory (RAM), Read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disk read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable Processing to obtain the program electronically and then store it in computer memory.
  • each part of the application may be implemented by hardware, software, firmware, or a combination thereof.
  • multiple steps or methods may be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if implemented in hardware, as in another embodiment, it may be implemented using any one or a combination of the following techniques known in the art: Discrete logic circuits, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.
  • each functional unit in each embodiment of the present application may be integrated into one processing module, or each unit may exist separately physically, or two or more units may be integrated into one module.
  • the above integrated modules may be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
  • the aforementioned storage medium may be a read-only memory, a magnetic disk, or an optical disk.

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Abstract

本申请公开了一种定位数据的处理方法及处理装置、计算设备和存储介质。定位数据的处理方法包括：获取移动目标按时序产生的第一定位数据序列；根据预设滤波算法对第一定位数据序列进行滤波处理并得到滤波数据序列，并对滤波数据序列进行吸附计算并得到吸附数据序列，预设滤波算法是根据吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；输出滤波数据序列得到移动目标的第二定位数据序列；显示所述第二定位数据序列中的第二定位数据所对应的位置。将吸附数据序列用于对卡尔曼滤波算法进行修正，在按时序递推计算的过程中，在一定程度上消除移动目标定位偏移，特别是定位缓慢偏移所导致的误差累积带来的影响，使得第二定位数据序列中的第二定位数据所对应的位置准确地反映了移动目标的实际位置，从而提高定位导航的精度，提高用户对定位导航产品，如车载导航产品的体验。

Description

定位数据的处理方法及处理装置、计算设备和存储介质
本申请要求于2018年8月27日提交中国专利局、申请号为201810981602.8、发明名称为“定位数据的处理方法及处理装置、终端设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及导航技术领域，尤其是涉及一种定位数据的处理方法及处理装置、计算设备和存储介质。
背景技术
相关技术中，基于全球卫星定位系统(Global Positioning System，GPS)的定位领域常用的定位算法包括：最小二乘法、扩展卡尔曼滤波算法、二阶扩展卡尔曼滤波算法等。最小二乘法通过线性拟合，将具有最小欧氏距离的点作为当前定位的估计。卡尔曼滤波算法首先根据状态方程来对目标当前的位置、速度、GPS接收机时钟差等进行预测；然后，根据这一状态预测先验估计值和卫星星历所提供的卫星位置和速度，卡尔曼滤波器就能够预测GPS接收机对各颗卫星的伪距和多普勒偏移值，这些测量值和接收机的实际测量值(观测值)之差又形成了测量残差；最后，卡尔曼滤波器通过对测量残差进行处理得到系统状态估计值的校正量以及校正之后的最佳估计值。
最小二乘法是最基本、最简单的定位算法，但是在动态多径等情形下，最小二乘法的定位效果非常不理想。卡尔曼滤波算法通过对按照时序产生的GPS定位数据进行迭代计算，虽然解决了最小二乘法在多径效应等场景下定位效果不理想的问题，但是由于它使用上一次的最佳估计作为当前预测先验估计值的计算基础，因此无法避免误差累积的现象。对于GPS缓慢偏离路线的情况，现有卡尔曼滤波算法存在由于误差累积导致无法有效处理GPS缓慢漂移的问题，定位效果并不理想。
发明内容
本申请的实施方式提供一种定位数据的处理方法及处理装置、计算设备和 存储介质。
本申请实施方式的定位数据的处理方法包括：获取移动目标按时序产生的第一定位数据序列；根据预设滤波算法对所述第一定位数据序列进行滤波处理并得到滤波数据序列，且对所述滤波数据序列进行吸附计算并得到吸附数据序列，所述预设滤波算法是根据所述吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；输出所述滤波数据序列得到所述移动目标的第二定位数据序列；显示所述第二定位数据序列中的第二定位数据所对应的位置。
本申请实施方式的定位数据的处理装置包括：获取单元，用于获取移动目标按时序产生的第一定位数据序列；处理单元，用于根据预设滤波算法对所述第一定位数据序列进行滤波处理并得到滤波数据序列，且对所述滤波数据序列进行吸附计算并得到吸附数据序列，所述预设滤波算法是根据所述吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；和输出单元，用于输出所述滤波数据序列得到所述移动目标的第二定位数据序列，并显示所述第二定位数据序列中的第二定位数据所对应的位置。
本申请实施方式的计算设备包括存储器和处理器，所述存储器中存储有可执行程序，所述可执行程序被所述处理器执行时，所述处理器执行上述实施方式所述的定位数据的处理方法。
本申请实施方式的计算机可读存储介质存储有可执行程序，所述可执行程序被处理器执行时，所述处理器执行上述实施方式所述的定位数据的处理方法。
本申请实施方式的定位数据的处理方法和处理装置、计算设备和计算机可读存储介质中，根据预设滤波算法对第一定位数据序列进行滤波处理得到滤波数据序列，并对滤波数据序列进行吸附计算，将吸附计算得到的吸附数据序列用于对卡尔曼滤波算法进行修正，在按时序递推计算的过程中，可以输出滤波数据序列得到移动目标的第二定位数据序列，并显示所述第二定位数据序列中的第二定位数据所对应的位置。输出的第二定位数据序列中，在一定程度上消除了移动目标定位偏移，特别是定位缓慢偏移所导致的误差累积带来的不利影响，使得第二定位数据序列中的第二定位数据所对应的位置准确地反映了移动目标的实际位置，从而提高定位导航的精度，提高用户对定 位导航产品，如车载导航产品和移动终端的满意度。
本申请的实施方式的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实施方式的实践了解到。
附图简要说明
本申请的上述和/或附加的方面和优点从结合下面附图对实施方式的描述中将变得明显和容易理解，其中：
图1是本申请实施方式的硬件环境示意图。
图2是本申请实施方式的定位数据的处理方法的流程图。
图3是本申请实施方式的定位数据的处理装置的模块示意图。
图4是本申请实施方式的定位数据的处理方法的另一流程图。
图5是本申请实施方式的定位数据的处理方法的又一流程图。
图6是本申请实施方式的定位数据的处理方法的再一流程图。
图7是本申请实施方式的终端设备的模块示意图。
图8是本申请实施方式的一种第一定位数据序列的轨迹示意图。
图9是原始卡尔曼算法对图8的第一定位数据序列进行处理后输出的定位数据序列的轨迹示意图。
图10是本申请实施方式的定位数据的处理方法对图8的第一定位数据序列进行处理后输出的第二定位数据序列的轨迹示意图。
主要元件符号说明：
终端100、定位数据的处理装置10、获取单元12、处理单元14、吸附模块142、滤波模块144、输出单元16、初始化单元18、定位系统200、服务器300、终端设备400、存储器410、处理器420。
具体实施方式
下面详细描述本申请的实施方式，所述实施方式的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施方式是示例性的，仅用于解释本申请，而不能理解为对本申请的限制。
下文的公开提供了许多不同的实施方式或例子用来实现本申请的不同结构。为了简化本申请的公开，下文中对特定例子的部件和设置进行描述。当然，它们仅仅为示例，并且目的不在于限制本申请。此外，本申请可以在不同例子中重复参考数字和/或参考字母，这种重复是为了简化和清楚的目的，其本身不指示所讨论各种实施方式和/或设置之间的关系。
请参阅图1，本申请实施方式的定位数据的处理方法可以应用于由定位系统200、服务器300和终端100所构成的硬件环境和/或软件环境中。终端100可以从定位系统200获取定位数据。终端100可以通过有线或无线网络与定位系统200和服务器300进行连接，上述网络包括但不限于：广域网、城域网或局域网，终端100包括但不限定于PC、手机、平板电脑、车载终端等。车载终端比如为车载导航设备。本申请实施例的定位数据的处理方法可以由服务器300来执行，也可以由终端100来执行，还可以是由服务器300和终端100共同执行。其中，终端100执行本申请实施例的定位数据的处理方法时，可在终端100安装客户端应用软件来共同执行。客户端应用软件可为导航软件或地图软件。在本申请实施例中，终端100和服务器300也可以称为计算设备。
请一并参阅图2和图3，本申请实施方式的定位数据的处理方法包括：
步骤S1，获取移动目标按时序产生的第一定位数据序列；
步骤S2，根据预设滤波算法对第一定位数据序列进行滤波处理并得到滤波数据序列，且对滤波数据序列进行吸附计算并得到吸附数据序列，预设滤波算法是根据吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；和
步骤S3，输出滤波数据序列得到移动目标的第二定位数据序列。
步骤S4，显示所述第二定位数据序列中的第二定位数据所对应的位置。
本申请实施方式的定位数据的处理方法可以应用于本申请实施方式的定位数据的处理装置10，也即是说，本申请实施方式的定位数据的处理装置10可以利用本申请实施方式的定位数据的处理方法对第一定位数据序列进行处理以得到移动目标的第二定位数据序列，获取移动目标更准确的定位信息。在本申请实施例中，定位数据的处理装置10可以为一种具有定位数据处理功能的独立的计算设备，例如终端设备或服务器，或者也可以为安装在计算设 备中的部件，通过运行计算设备而实现其定位数据的处理功能。在本申请实施例中，独立的终端设备可以为车载导航设备或其它具有导航功能的设备。
本申请实施方式的定位数据的处理装置10包括：获取单元12、处理单元14和输出单元16。获取单元12用于获取移动目标按时序产生的第一定位数据序列。处理单元14用于根据预设滤波算法对第一定位数据序列进行滤波处理并得到滤波数据序列，且对滤波数据序列进行吸附计算并得到吸附数据序列。输出单元16用于输出滤波数据序列得到移动目标的第二定位数据序列。
具体地，获取单元12可以实现步骤S1，在移动目标移动过程中通过GPS对移动目标进行定位，也即是说，获取单元12可以发送定位指令给GPS定位系统(例如定位系统200)，接收GPS定位系统返回的定位数据，即获取移动目标按时序产生的第一定位数据序列，并将第一定位数据序列发送给处理单元14。其中，第一定位数据序列包括多个时序的第一定位数据，第一定位数据包括移动目标的移动速度、偏向角度、第一位置数据(例如，经度和纬度)和定位精度等数据。
由于GPS信号可能受到大气层干扰、卫星时钟可能存在误差、可能存在多径效应等问题，导致定位数据存在噪声，如此，处理单元14可以通过实现步骤S2，对获取单元12获取的第一定位数据序列进行滤波处理，减小第一定位数据中的噪声。处理单元14对按时序产生的第一定位数据序列中的第一定位数据逐一进行滤波处理，以得到滤波数据序列。
进一步地，处理单元14可以对滤波数据序列进行吸附计算，将吸附计算得到的吸附数据序列用于对卡尔曼滤波算法进行修正，使得按时序递推计算过程中，滤波处理得到的滤波数据序列的噪声减小，并将滤波数据发送至输出单元16。输出单元16可以实现步骤S3，输出滤波数据序列得到移动目标的第二定位数据序列。进一步地，输出单元16将第二定位数据序列输出到终端设备的显示装置，由显示装置显示第二定位数据序列中的第二定位数据所对应的位置。在本申请实施例中，显示装置也可以是输出单元16的一部分，显示第二定位数据对应的位置的步骤可以是显示装置在接收到第二定位数据序列后被触发而执行的，也可以是显示装置接收到用户通过按键或触摸屏等输入设备输入的指令后而执行的。输出的第二定位数据序列中，在一定程度 上消除了移动目标定位偏移，特别是定位缓慢偏移所导致的误差累积带来的不利影响，使得第二定位数据序列中的第二定位数据所对应的位置准确地反映了移动目标的实际位置，从而提高定位导航的精度，提高用户对定位导航产品，如车载导航设备和移动终端的满意度。
在一个实施例中，第一定位数据序列可以是原始定位数据序列，即通过GPS定位系统获取的原始定位数据序列，第一定位数据可以是原始定位数据。第二定位数据序列可以是通过预设滤波算法对第一定位数据序列进行滤波处理后得到的定位数据序列，第二定位数据可以是通过预设滤波算法对第一定位数据进行滤波处理后得到的定位数据。
卡尔曼滤波算法是一种线性系统状态方程，是通过系统输入观测数据，对观测数据进行处理，再输出处理结果，实现对系统进行最佳估计的算法。由于观测数据包括系统中的噪声，因而最佳估计也即为对系统中的噪声信号进行滤波的过程。
在原始卡尔曼滤波算法中，根据上一轮滤波处理得到的最佳估计值进行预测计算得到当前滤波处理的预测先验估计值。但是这样预测得到的预测先验估计值存在不确定性，因此，可以利用当前滤波处理所获取的观测数据和当前滤波处理的预测先验估计值来共同计算得到滤波后的最佳估计值，在对定位数据的滤波处理中，最佳估计值就是定位数据的滤波结果，使得滤波后定位数据的噪声减小。
在具有多个时序的第一定位数据序列中，利用卡尔曼滤波算法对第一定位数据进行滤波处理的过程也即是对第一定位数据进行迭代计算。
在本申请的定位数据的处理方法中，根据吸附数据序列对卡尔曼滤波算法进行修正，使得利用卡尔曼滤波算法对第一定位数据序列进行滤波的过程中由于GPS缓慢漂移产生的误差累积进一步减小，进而得到的第二定位数据序列更加准确，有利于提高定位精度。
定位数据的处理装置10的一部分功能可以由上述服务器300或终端100实现，另一部分功能可由上述终端100或服务器300实现，或另一部分功能由独立于上述服务器300和终端100的设备来实现。定位数据的处理装置10的全部功能也可由上述服务器300或终端100单独实现，或由独立于上述服 务器300和终端100的设备来实现。
第一定位数据序列可包括按j个时序产生的j个第一定位数据，j为自然数且j>＝2，在对第一定位数据序列进行滤波时，可建立如下的卡尔曼模型和方程式：
x k＝F kx k-1+B ku k+w k
其中，x k是系统状态的第k个时序的最佳估计值，x k-1是系统状态的第k-1个时序的最佳估计值；F k是状态转移模型，B k是第k个时序的系统控制输入，u k是第k个时序的系统控制向量，w k是过程噪声，k为自然数且1<k<＝j。
并建立如下的观测模型和方程式：
z k＝H kx k+v k
其中，z k为第k个时序真实状态空间内系统状态的真实值x k的观测值，H k为观测模型，v k为观测噪声。
在某些实施方式中，第一定位数据序列包括第一位置数据序列，步骤S2包括：
根据预设滤波算法对第一位置数据序列进行滤波处理并得到滤波数据序列，并对滤波数据序列进行吸附计算并得到吸附数据序列。
如此，可以对移动目标的第一位置数据序列进行滤波，例如对移动目标的经度和纬度进行滤波，减小移动目标第一位置数据的噪声。
对于定位数据的处理装置10来说，处理单元14可以用于根据预设滤波算法对第一位置数据序列进行滤波处理并得到滤波数据序列，并对滤波数据序列进行吸附计算并得到吸附数据序列。
也即是说，在本申请实施方式中，可以对移动目标的第一位置数据进行滤波，而滤波数据包括滤波位置数据，第k个时序的最佳估计值x k和第k-1个时序的最佳估计值x k-1分别是移动目标第k个时序的滤波位置数据和第k-1个时序的滤波位置数据，具体地，第一位置数据和滤波位置数据可以是移动目标的经度和纬度，在卡尔曼滤波算法中以二维矩阵的形式进行滤波计算。
状态转移模型H k作用于第k-1个时序的移动目标的位置数据的最佳估计值x k-1。第k-1个时序吸附计算得到的吸附数据和根据第k-1个时序的移动目标的移动速度和偏向角度计算的位移，可以作为系统控制输入B k作用于控制 向量u k，也即是说，本申请实施方式中，卡尔曼模型和方程式根据吸附数据进行修正。具体地，第k个时序的卡尔曼模型和方程式根据第k-1个时序计算得到的吸附数据进行修正，然后对第k个时序的第一定位数据进行滤波处理。
在滤波过程中，假设过程噪声w k呈正态分布且均值为0，过程噪声的误差协方差为Q k，即：w k～N(0，Q k)。过程噪声的误差协方差是过程噪声的不确定度。
在观测方程式中，移动目标的位置数据的观测值z k可以认为是真实状态空间中移动目标的位置数据的真实值x k通过观测模型H k映射到观测空间得到的观测值。滤波过程中，假设观测噪声v k呈正态分布且均值为0，观测噪声的误差协方差为R k，即：v k～N(0，R k)。观测噪声的误差协方差是观测噪声的不确定度。
请参阅图4，在某些实施方式中，第一个时序的滤波数据可以利用第一个时序的第一定位数据获取，第一个时序的吸附数据可以利用第一个时序的滤波数据进行吸附计算得到。
对于定位数据的处理装置10来说，定位数据的处理装置10包括初始化单元18，处理单元14包括吸附模块142，初始化单元18可以用于利用第一个时序的第一定位数据获取第一个时序的滤波数据。其中，第一个时序为获取单元12按时序获取的定位数据序列中的第一个定位数据对应的时序。吸附模块142可以用于利用第一个时序的滤波数据进行吸附计算得到第一个时序的吸附数据。
在某些实施方式中，滤波数据包括最佳估计值和最佳估计值的误差协方差。第二定位数据序列可以通过依次输出每个时序的最佳估计值得到。
对于定位数据的处理装置10来说，初始化单元18可以用于利用第一个时序的第一定位数据获取第一个时序的最佳估计值和第一个时序的最佳估计值的误差协方差。进而输出单元16可以输出第一个时序的最佳估计值为第一个时序的第二定位数据。
具体地，吸附模块142可以用于利用第一时序的最佳估计值进行吸附计算得到第一个时序的吸附数据。其中，处理单元14可以将第一个时序的滤波数据用于第二个时序的第一定位数据的滤波处理。在对第二个时序的第一定位数据进行滤波处理时，处理单元14将吸附数据用于修正卡尔曼滤波算法。
可以理解，在定位数据的处理过程中，第一个时序的第一定位数据的滤波处理由于没有与第一定位数据的上一轮滤波处理相关的滤波数据和吸附数据，因此，初始化单元18可以利用第一个时序的第一定位数据获取第一个时序的最佳估计值和第一个时序的最佳估计值的误差协方差，即根据第一个时序的第一定位数据设置第一个时序的最佳估计值和第一个时序的最佳估计值的误差协方差。
具体地，第一个时序的最佳估计值设置为：
Figure PCTCN2019102211-appb-000001
其中，m、n分别是移动目标第一个时序的经度和纬度。
第一个时序的最佳估计值的误差协方差设置为：
Figure PCTCN2019102211-appb-000002
其中，p为移动目标第一个时序的GPS定位数据的定位精度。
在某些实施方式中，初始化单元18用于根据经验值和统计值对状态转移模型、观测模型和过程噪声的误差协方差进行初始化。
可以理解，在卡尔曼滤波过程中，每一个时序的第一定位数据都会利用状态转移模型、观测模型和过程噪声的误差协方差进行滤波计算，且状态转移模型、观测模型和过程噪声的误差协方差在递推计算的过程中均保持不变，如此，可以通过初始化单元18对状态转移模型、观测模型和过程噪声的误差协方差进行初始化以确定它们的值，方便处理单元14对第一定位数据进行滤波处理。
具体地，状态转移模型初始化为：
Figure PCTCN2019102211-appb-000003
观测模型初始化为：
Figure PCTCN2019102211-appb-000004
过程噪声的误差协方差初始化为：
Figure PCTCN2019102211-appb-000005
其中，状态转移模型、观测模型和过程噪声的误差协方差根据经验值和统计值确定，上述状态转移模型、观测模型和过程噪声的误差协方差的初始化结果仅作为实施例，不能解释为对本申请的限定，在其他实施方式中，状态 转移模型、观测模型和过程噪声的误差协方差的值可以根据实际需要进行变换，在此不做具体限定。
请参阅图5和图6，在某些实施方式中，第k个时序的滤波数据可以利用第k个时序的第一定位数据、第k-1个时序的第一定位数据、第k-1个时序的滤波数据和第k-1个时序的吸附数据并通过卡尔曼滤波算法计算得到，第k个时序的吸附数据可以利用第k个时序的滤波数据进行吸附计算得到。
对于定位数据的处理装置10来说，处理单元14包括滤波模块144，滤波模块144用于利用第k个时序的第一定位数据、第k-1个时序的第一定位数据、第k-1个时序的滤波数据和第k-1个时序的吸附数据并通过卡尔曼滤波算法计算得到第k个时序的滤波数据。吸附模块142用于利用第k个时序的滤波数据进行吸附计算得到第k个时序的吸附数据。在递推计算的过程中，处理单元14将吸附数据用于修正卡尔曼滤波算法。
例如，第二个时序的滤波数据可以是滤波模块144利用第二个时序的第一定位数据、第一个时序的滤波数据和第一个时序的吸附数据并通过卡尔曼滤波算法计算得到。第二个时序的吸附数据可以是吸附模块142利用第二个时序的滤波数据进行吸附计算得到。
在某些实施方式中，输出单元16可以输出第k个时序的最佳估计值为第k个时序的第二定位数据。如此，输出单元16按时序依次输出滤波数据序列的每一个时序的最佳估计值得到移动目标的第二定位数据。
请参阅图6，在某些实施方式中，滤波数据包括预测先验估计值、预测先验估计值的误差协方差和卡尔曼增益，第k个时序的预测先验估计值可以利用第k-1个时序的第一位置数据、第k-1个时序的最佳估计值和第k-1个时序的吸附数据计算得到，
第k个时序的预测先验估计值的误差协方差可以利用第k-1个时序的最佳估计值的误差协方差计算得到。
对于定位数据的处理装置10来说，滤波模块144可以用于利用第k-1个时序的第一位置数据、第k-1个时序的最佳估计值和第k-1个时序的吸附数据并通过上述卡尔曼模型和方程式计算得到第k个时序的预测先验估计值，即处理单元14将第k-1个时序的吸附数据用于修正卡尔曼滤波算法，以对第k 个时序的第一位置数据进行滤波处理。
滤波模块144可以用于利用第k-1个时序的最佳估计值的误差协方差并通过上述卡尔曼模型和方程式计算得到第k个时序的预测先验估计值的误差协方差，以评估第k个时序的预测先验估计值的不确定度。
具体地，第k个时序的预测先验估计值根据下列方程式计算：
Figure PCTCN2019102211-appb-000006
其中，
Figure PCTCN2019102211-appb-000007
是移动目标第k个时序的预测先验估计值，
Figure PCTCN2019102211-appb-000008
是移动目标第k-1个时序的最佳估计值。第k-1个时序的第一定位数据中移动目标的移动速度和偏向角度以及第k-1个时序的吸附数据可以作为系统控制输入B k作用于控制向量u k，即利用吸附数据对卡尔曼滤波算法进行修正。在计算过程中，假设过程噪声的值为0，过程噪声的误差协方差为Q k。
进一步地，第k个时序的预测先验估计值的误差协方差根据下列方程式计算：
Figure PCTCN2019102211-appb-000009
其中，P k|k-1是移动目标第k个时序的预测先验估计值
Figure PCTCN2019102211-appb-000010
的误差协方差，P k-1|k-1是移动目标第k-1个时序的的最佳估计值
Figure PCTCN2019102211-appb-000011
的误差协方差，其中，过程噪声的误差协方差为：
Figure PCTCN2019102211-appb-000012
也即是说，过程噪声的误差协方差在第一定位数据迭代计算的过程中保持不变。
滤波模块144用于通过上述方程式计算得到移动目标第k个时序的预测先验估计值和第k个时序的预测先验估计值的误差协方差。
在某些实施方式中，滤波数据包括残差和残差的误差协方差，第k个时序的残差可以利用第k个时序的第一定位数据和第k个时序的预测先验估计值并通过观测模型计算得到。
第k个时序的残差的误差协方差可以利用第k个时序的第一定位数据和第k个时序的预测先验估计值的误差协方差并通过观测模型计算得到。
对于定位数据的处理装置10来说，滤波模块144可以用于利用第k个时序的第一定位数据和第k个时序的预测先验估计值并通过观测模型计算得到 第k个时序的残差。并且，滤波模块144可以用于利用第k个时序的第一定位数据和第k个时序的预测先验估计值的误差协方差并通过观测模型计算得到第k个时序的残差的误差协方差
具体地，第k个时序的残差可通过下列方程式计算：
Figure PCTCN2019102211-appb-000013
其中，
Figure PCTCN2019102211-appb-000014
是移动目标的位置数据在第k个时序的残差，z k是移动目标的观测值。计算第k个时序的残差时，观测模型H k作用于第k个时序的预测先验估计值
Figure PCTCN2019102211-appb-000015
并将第k个时序的预测先验估计值
Figure PCTCN2019102211-appb-000016
映射到与观测值z k相同的观测空间，以方便计算第k个时序的残差。在计算的过程中，假设观测噪声为0，观测噪声的误差协方差为R k。具体地，观测值z k为GPS定位数据中移动目标第k个时序的第一位置数据，即
Figure PCTCN2019102211-appb-000017
其中，m k、n k分别是移动目标第k个时序的经度和纬度。
进一步地，第k个时序的残差的误差协方差可通过下列方程式计算：
Figure PCTCN2019102211-appb-000018
其中，S k是移动目标的位置数据在第k个时序的残差
Figure PCTCN2019102211-appb-000019
的误差协方差。其中，第k个时序的观测噪声的误差协方差R k根据GPS第k个时序的第一定位数据的定位精度确定，即
Figure PCTCN2019102211-appb-000020
其中，p k是第k个时序的GPS定位数据的定位精度。
在某些实施方式中，第k个时序的卡尔曼增益可以利用第k个时序的第一位置数据、第k个时序的预测先验估计值和第k个时序的预测先验估计值的误差协方差计算得到。进一步地，第k个时序的卡尔曼增益可以利用第k个时序的预测先验估计值的误差协方差和第k个时序的残差的误差协方差并通过观测模型计算得到。
对于定位数据的处理装置10来说，滤波模块144可以用于利用第k个时序的第一位置数据、第k个时序的预测先验估计值和第k个时序的预测先验估计值的误差协方差并通过上述卡尔曼模型和方程式以及观测模型计算得到第k个时序的卡尔曼增益。进一步地，滤波模块144可以用于利用第k个时 序的预测先验估计值的误差协方差和第k个时序的残差的误差协方差并通过观测模型计算得到第k个时序的卡尔曼增益。
具体地，第k个时序的卡尔曼增益可通过下列方程式计算：
Figure PCTCN2019102211-appb-000021
其中，K k是移动目标的位置数据在第k个时序的卡尔曼增益。
在某些实施方式中，第k个时序的最佳估计值可以利用第k个时序的第一位置数据、第k个时序的预测先验估计值和第k个时序的卡尔曼增益计算得到，进一步地，第k个时序的最佳估计值可以利用第k个时序的预测先验估计值、第k个时序的卡尔曼增益和第k个时序的残差计算得到。
第k个时序的最佳估计值的误差协方差可以利用第k个时序的预测先验估计值的误差协方差和第k个时序的卡尔曼增益计算得到。
对于定位数据的处理装置10来说，滤波模块144可以用于利用第k个时序的第一位置数据、第k个时序的预测先验估计值和第k个时序的卡尔曼增益计算得到第k个时序的最佳估计值。其中，输出单元16可以将第k个时序的最佳估计值输出为第k个时序的第二定位数据。且第k个时序的最佳估计值可以用于后续时序的第一定位数据的滤波处理。
滤波模块144可以用于利用第k个时序的预测先验估计值的误差协方差和第k个时序的卡尔曼增益计算得到第k个时序的最佳估计值的误差协方差。第k个时序的最佳估计值的误差协方差可以用于后续时序的第一定位数据的滤波处理。
具体地，第k个时序的最佳估计值可通过下列方程式计算：
Figure PCTCN2019102211-appb-000022
其中，
Figure PCTCN2019102211-appb-000023
是移动目标第k个时序的最佳估计值。
进一步地，第k个时序的最佳估计值的误差协方差可通过下列方程式计算：
Figure PCTCN2019102211-appb-000024
其中，P k|k是移动目标第k个时序的最佳估计值
Figure PCTCN2019102211-appb-000025
的误差协方差，I是单位矩阵。
在本申请的实施方式中，对第k个时序的第一位置数据进行滤波处理时， 得到的最佳估计值可以认为是移动目标第k个时序的第二位置数据，从而输出单元16可以将最佳估计值输出为第二定位数据。在按时序递推过程中，按j个时序产生的j个第一定位数据经过滤波处理后可以由输出单元16按j个时序输出得到j个第二定位数据，即输出得到移动目标的第二定位数据序列。
在上述的实施方式中，处理单元14可以根据GPS定位数据的定位精度确定第一个时序的最佳估计值的误差协方差和观测噪声的误差协方差，进而根据第一个时序的最佳估计值的误差协方差递推计算后续时序的预测先验估计值的误差协方差，及根据各个时序的观察噪声和各个时序的预测先验估计值的误差协方差计算对应时序的最佳估计值的误差协方差。
在某些实施方式中，第一定位数据包括定位精度、定位信号强度和定位的卫星数目中的至少一种，可以根据定位精度、定位信号强度和定位的卫星数目中的至少一种计算各个时序的预测先验估计值的误差协方差和最佳估计值的误差协方差。
也即是说，处理单元14还可以使用定位信号强度或定位卫星数目确定第一定位数据序列中第一个时序的最佳估计值的误差协方差和观测噪声的误差协方差，或使用定位精度、定位信号强度和定位卫星数目的两个或两个以上组合确定第一定位数据序列的第一个时序的最佳估计值的误差协方差和观测噪声的误差协方差。进一步地，根据第一个时序的最佳估计值的误差协方差递推计算后续时序的预测先验估计值误差协方差，及根据各个时序的观察噪声和各个时序的预测先验估计值的误差协方差计算对应时序的最佳估计值的误差协方差。
可以理解，在导航场景中，定位数据的处理装置10通常可以为移动目标在导航前设置规划路线，因此，吸附模块142可以用于判断第k个时序的第二定位数据所对应的位置是否偏离规划路线，以对移动目标进行导航，使得移动目标沿规划路线移动。
在某些实施方式中，对于第一个时序的第二定位数据，吸附模块142可以用于判断第一个时序的第二定位数据所对应的位置是否偏离规划路线。第一个时序的吸附数据可以在第一个时序的第二定位数据所对应的位置未偏离规划路线时，利用第一个时序的滤波数据进行吸附计算得到。
对于定位数据的处理装置10来说，吸附模块142用于在第一个时序的第二定位数据所对应的位置未偏离规划路线时，利用第一个时序的滤波数据进行吸附计算得到第一个时序的吸附数据。
具体地，在导航场景中，开启导航后获取单元12获取移动目标按时序产生的定位数据序列。而在开启导航前可以预先设置好规划路线，在导航开启后，判断第一个时序的第二定位数据所对应的位置是否偏离规划路线。这样，可以避免开启导航时，移动目标的第一个时序的位置偏离规划路线而导致导航出错。
在某些实施方式中，在第一个时序的第二定位数据所对应的位置偏离规划路线时，根据第一个时序的滤波数据对规划路线进行更新。
对于定位数据的处理装置10来说，吸附模块142用于在第一个时序的第二定位数据所对应的位置未偏离规划路线时，根据第一个时序的滤波数据更新规划路线。
在某些实施方式中，第k个时序的吸附数据可以在第k个时序的第二定位数据所对应的位置未偏离规划路线时，利用第k个时序的滤波数据进行吸附计算得到。
对于定位数据的处理装置10来说，吸附模块142用于在第k个时序的第二定位数据所对应的位置未偏离规划路线时，利用第k个时序的滤波数据进行吸附计算得到第k个时序的吸附数据。
在第k个时序的第二定位数据所对应的位置未偏离规划路线时，可以认为第k个时序移动目标的位置在规划路线上，此时，计算第k个时序的第二定位数据在规划路线的对应位置，得到第k个时序的吸附数据。第k个时序的吸附数据可以作为计算后续时序的预测先验估计值的控制输入，即用于对卡尔曼滤波算法进行修正以对后续时序的第一定位数据进行滤波处理。
在某些实施方式中，第k个时序的吸附数据可以利用第k个时序的最佳估计值进行吸附计算得到。
对于定位数据的处理装置10来说，吸附模块142可以用于利用第k个时序的最佳估计值进行吸附计算得到第k个时序的吸附数据。
可以理解，第k个时序的最佳估计值由输出单元16输出得到第k个时序 的第二定位数据，在其他实施方式中，第k个时序的吸附数据可以利用第k个时序的第二定位数据进行吸附计算得到。相应地，吸附模块142用于利用第k个时序的第二定位数据进行吸附计算得到第k个时序的吸附数据。同样地，可以通过吸附计算得到第k个时序的第二定位数据在规划路线上的对应位置。
在某些实施方式中，在第k个时序的第二定位数据所对应的位置偏离规划路线时，根据第k个时序的第二定位数据对规划路线进行更新。
对于定位数据的处理装置10来说，吸附模块142可以用于在移动目标的第k个时序的第二定位数据所对应的位置偏离规划路线时，根据第k个时序的第二定位数据所对应的位置更新规划路线。如此，可以在移动目标偏离规划路线时，对规划路线进行实时更新，确保导航过程中，移动目标可以到达目标位置。
其中，更新规划路线时，以第二定位数据所对应的位置作为起点，重新确定规划路线。
具体地，判断第k个时序的第二定位数据是否偏离规划路线时，采集连续的多个时序的第二定位数据进行判断，若多个时序的第二定位数据所对应的位置均偏离规划路线，则可以认为移动目标偏离规划路线，再根据第k个时序的第二定位数据所对应的位置更新规划路线。
请参阅图7，本申请实施方式的终端设备400包括存储器410和处理器420，存储器410中存储有可执行程序，可执行程序被处理器420执行时，处理器420执行上述任一实施方式的定位数据的处理方法。
例如，可执行程序被处理器420执行时，处理器420可以执行以下步骤：
步骤S1，获取移动目标按时序产生的第一定位数据序列；
步骤S2，根据预设滤波算法对第一定位数据序列进行滤波处理并得到滤波数据序列，且对滤波数据序列进行吸附计算并得到吸附数据序列，预设滤波算法是根据吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；和
步骤S3，输出滤波数据序列得到移动目标的第二定位数据序列。
步骤S4，显示所述第二定位数据序列中的第二定位数据所对应的位置。
在本申请实施例中，处理器420可以进一步执行以下步骤：显示第二定位 数据序列中的第二定位数据所对应的位置。
上述终端设备中，处理器420执行上述任一实施方式的定位数据的处理方法，用于实现移动目标的定位导航，将吸附数据序列用于对卡尔曼滤波算法进行修正，在按时序递推计算的过程中，可以得到移动目标的第二定位数据序列，从而在一定程度上消除移动目标定位偏移，特别是定位缓慢偏移所导致的误差累积带来的影响，使得第二定位数据序列中的第二定位数据所对应的位置准确地反映了移动目标的实际位置，从而提高定位导航的精度，提高用户对终端设备400的满意度。
在某些实施方式中，终端设备400可以是上述终端100。终端设备400可以和定位系统200以及服务器300通信连接构成导航系统。
在某些实施方式中，上述定位数据的处理装置10可以设置在终端设备400中，具体地，定位数据的处理装置10可以是终端设备400的一个独立功能部件，或定位数据的处理装置10的部分功能模块可以由处理器420实现，或定位数据的处理装置10的各个功能模块均由处理器420实现。
在上述实施例中个，定位数据的处理方法由终端设备400实现，在另一实施例中，该方法也可以由服务器或其它独立的设备实现，该服务器或其它独立的设备可以具有与图7所示类似的结构，即包括存储器和处理器，存储器中存储有可执行程序，可执行程序被处理器执行时，处理器执行上述任一实施方式的定位数据的处理方法。
本申请实施方式的计算机可读存储介质存储有可执行程序，可执行程序被处理器420执行时，处理器420执行上述任一实施方式的定位数据的处理方法。
例如，可执行程序被处理器420执行时，处理器420可以执行以下步骤：
步骤S1，获取移动目标按时序产生的第一定位数据序列；
步骤S2，根据预设滤波算法对第一定位数据序列进行滤波处理并得到滤波数据序列，且对滤波数据序列进行吸附计算并得到吸附数据序列，预设滤波算法是根据吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；和
步骤S3，输出滤波数据序列得到移动目标的第二定位数据序列。
步骤S4，显示所述第二定位数据序列中的第二定位数据所对应的位置。
在本申请实施例中，处理器420可以进一步执行以下步骤：显示第二定位数据序列中的第二定位数据所对应的位置。
请一并参阅图8、图9和图10，图示是终端设备显示的部分轨迹示意图，包括道路轨迹和定位数据的轨迹，其中，图中的各个点表示各个时序的定位数据所对应的位置。图8所示的各个点表示各个时序的第一定位数据序列的轨迹，轨迹中的每一个点对应移动目标的一个时序的第一定位数据。如图9所示的各个点表示利用原始卡尔曼滤波算法对图8所示的第一定位数据序列进行滤波处理后得到的定位数据序列的轨迹。如图10所示的各个点表示利用本申请实施方式的定位数据的处理方法对图8所述的第一定位数据序列进行处理后的第二定位数据序列的轨迹。
如图8所示，第一定位数据所对应的位置存在移动目标位于道路之外的情况，在实际应用中，特别是车辆的行驶过程中，车辆通常沿道路行驶，也即是说图8所示的第一定位数据中存在噪声。
如图9所示，图示的定位数据序列的轨迹中的每一个点对应移动目标的一个时序的第一定位数据经过滤波处理后得到的定位数据。利用原始卡尔曼滤波算法进行滤波处理后得到的定位数据所对应的位置依然存在位于道路之外的情况，因此，原始卡尔曼滤波算法未能很好消除第一定位数据中的噪声。
如图10所示，第二定位数据序列的轨迹中的每一个点对应移动目标的一个时序的第二定位数据，图中的轨迹与道路能够较好的重合，表示第二定位数据中的噪声较小，即本申请实施方式的定位数据的处理方法可以有效地消除定位数据中的噪声，移动目标经滤波处理后的第二定位数据序列的轨迹符合道路轨迹，得到的第二定位数据序列中的第二定位数据所对应的位置能够准确地反映移动目标的实际位置，使得定位导航更加准确可靠。
在本说明书的描述中，参考术语“某些实施方式”、“一个实施方式”、“一些实施方式”、“示意性实施方式”、“示例”、“具体示例”、或“一些示例”等的描述意指结合所述实施方式或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施方式或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施方式或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施方式或示例中以合适的方 式结合。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现特定逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本申请各个实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。上述提到的存储介质可以是只读存储器，磁盘或光盘等。
尽管上面已经示出和描述了本申请的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本申请的限制，本领域的普通技术人员在本申请的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (15)

  1. 一种定位数据的处理方法，由计算设备执行，包括：
    获取移动目标按时序产生的第一定位数据序列；
    根据预设滤波算法对所述第一定位数据序列进行滤波处理并得到滤波数据序列，且对所述滤波数据序列进行吸附计算并得到吸附数据序列，所述预设滤波算法是根据所述吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；
    输出所述滤波数据序列得到所述移动目标的第二定位数据序列；和
    显示所述第二定位数据序列中的第二定位数据所对应的位置。
  2. 如权利要求1所述的定位数据的处理方法，其中，所述第一定位数据序列包括第一位置数据序列，根据预设滤波算法对所述第一定位数据序列进行滤波处理并得到滤波数据序列，并对所述滤波数据序列进行吸附计算并得到吸附数据序列包括：
    根据所述预设滤波算法对所述第一位置数据序列进行滤波处理并得到滤波数据序列，并对所述滤波数据序列进行吸附计算并得到吸附数据序列。
  3. 如权利要求1所述的定位数据的处理方法，其中，所述第一定位数据序列包括按j个时序产生的j个第一定位数据，j为自然数且j>＝2，第k个时序的滤波数据是利用第k个时序的所述第一定位数据、第k-1个时序的所述第一定位数据、第k-1个时序的所述滤波数据和第k-1个时序的吸附数据通过卡尔曼滤波算法计算得到，第k个时序的所述吸附数据是利用第k个时序的所述滤波数据进行吸附计算得到，其中，k为自然数且1<k<＝j。
  4. 如权利要求3所述的定位数据的处理方法，其中，第一个时序的所述滤波数据是利用第一个时序的所述第一定位数据获取，第一个时序的所述吸附数据是利用第一个时序的所述滤波数据进行吸附计算得到。
  5. 如权利要求3或4所述的定位数据的处理方法，其中，所述滤波数据包括最佳估计值和所述最佳估计值的误差协方差，所述第二定位数据序列是通过依次输出每个时序的所述最佳估计值得到。
  6. 如权利要求5所述的定位数据的处理方法，其中，所述第一定位数据 包括第一位置数据，所述滤波数据包括预测先验估计值、所述预测先验估计值的误差协方差和卡尔曼增益，
    第k个时序的所述预测先验估计值是利用第k-1个时序的所述第一位置数据、第k-1个时序的所述最佳估计值和第k-1个时序的所述吸附数据计算得到，
    第k个时序的所述预测先验估计值的误差协方差是利用第k-1个时序的所述最佳估计值的误差协方差计算得到，
    第k个时序的所述卡尔曼增益是利用第k时序的所述第一位置数据、第k个时序的所述预测先验估计值和第k个时序的所述预测先验估计值的误差协方差计算得到，
    第k个时序的所述最佳估计值是利用第k个时序的所述第一位置数据、第k个时序的所述预测先验估计值和第k个时序的所述卡尔曼增益计算得到，
    第k个时序的所述最佳估计值的误差协方差是利用第k个时序的所述预测先验估计值的误差协方差和第k个时序的所述卡尔曼增益计算得到。
  7. 如权利要求6所述的定位数据的处理方法，其中，第k个时序的所述吸附数据是利用第k个时序的所述最佳估计值进行吸附计算得到。
  8. 如权利要求6所述的定位数据的处理方法，其中，所述第一定位数据包括定位精度、定位信号强度和定位卫星数目中的至少一种，利用所述定位精度、所述定位信号强度和所述定位卫星数目中的至少一种计算各个时序的所述预测先验估计值的误差协方差和所述最佳估计值的误差协方差。
  9. 如权利要求1所述的定位数据的处理方法，其特中，所述第二定位数据序列包括按j个时序得到的j个第二定位数据，j为自然数且j>＝2，第k个时序的吸附数据是在第k个时序的所述第二定位数据所对应的位置未偏离规划路线时，利用第k个时序的所述滤波数据进行吸附计算得到，k为自然数且1<＝k<＝j。
  10. 如权利要求9所述的定位数据的处理方法，其中，所述规划路线在第k个时序的所述第二定位数据所对应的位置偏离所述规划路线时根据第k个时序的所述第二定位数据进行更新。
  11. 一种定位数据的处理装置，包括：
    获取单元，用于获取移动目标按时序产生的第一定位数据序列；
    处理单元，用于根据预设滤波算法对所述第一定位数据序列进行滤波处理并得到滤波数据序列，且对所述滤波数据序列进行吸附计算并得到吸附数据序列，所述预设滤波算法是根据所述吸附数据序列对卡尔曼滤波算法进行修正后得到的算法；和
    输出单元，用于输出所述滤波数据序列得到所述移动目标的第二定位数据序列，并显示所述第二定位数据序列中的第二定位数据所对应的位置。
  12. 如权利要求11所述的定位数据的处理装置，其中，所述第一定位数据序列包括第一位置数据序列，所述处理单元用于根据所述预设滤波算法对所述第一位置数据序列进行滤波处理并得到滤波数据序列，并对所述滤波数据序列进行吸附计算并得到吸附数据序列。
  13. 如权利要求11所述的定位数据的处理装置，其中，所述第一定位数据序列包括按j个时序产生的j个第一定位数据，j为自然数且j>＝2，所述处理单元包括吸附模块和滤波模块，所述滤波模块用于利用第k个时序的所述第一定位数据、第k-1个时序的所述第一定位数据、第k-1个时序的滤波数据和第k-1个时序的吸附数据通过卡尔曼滤波算法计算得到第k个时序的所述滤波数据；
    所述吸附模块用于利用第k个时序的所述滤波数据进行吸附计算得到第k个时序的所述吸附数据，其中，k为自然数且1<k<＝j。
  14. 一种计算设备，其特征在于，包括存储器和处理器，所述存储器中存储有可执行程序，所述可执行程序被所述处理器执行时，所述处理器执行如权利要求1至10中任一项所述的定位数据的处理方法。
  15. 一种计算机可读存储介质，存储有可执行程序，其特征在于，所述可执行程序被处理器执行时，所述处理器执行如权利要求1至10中任一项所述的定位数据的处理方法。
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