Positioning precision testing method, device and system

Abstract

The invention discloses a positioning precision testing method, device and system, and relates to the technical field of satellite positioning. According to the scheme, the method comprises the steps that observation track sequences synchronously collected through all the to-be-measured positioning devices in the moving process of the moving carrier are obtained; performing fusion processing on the observation track sequence to obtain positioning reference data; and comparing the at least one observation track sequence with the positioning reference data to obtain a positioning precision test result. Compared with a traditional scheme in which high-precision reference equipment with purchase cost must be used, the high-precision positioning reference is generated through track fusion of the vehicle-mounted low-cost positioning equipment, extra purchase of special reference equipment is effectively avoided, and the positioning precision test cost is remarkably reduced.

Description

Technical Field

[0001] The present application relates to the field of satellite positioning technology, and in particular to a positioning accuracy testing method, device and system. Background Art

[0002] The core operating principle of satellite positioning equipment is to obtain pseudorange observations by measuring satellite signal propagation delays and then interpret spatial position information based on satellite ephemeris parameters. This type of equipment has been widely used in areas such as in-vehicle navigation, mobile terminal positioning, geographic information mapping, and traffic monitoring, becoming a key technology for modern location-based services.

[0003] Currently, positioning accuracy testing involves comparing the coordinate outputs of the device under test with a reference positioning device of known higher accuracy to assess its accuracy. While this method can yield highly reliable test results, it relies on the use of a more accurate reference positioning device. Summary of the Invention

[0004] In view of this, the present application is dedicated to providing a positioning accuracy testing method, device and system that can get rid of the dependence on higher precision positioning equipment during positioning accuracy testing.

[0005] According to the first aspect of the present application, a positioning accuracy test method is provided, wherein at least two positioning devices to be measured are provided on a mobile carrier; the method comprises: obtaining an observation trajectory sequence synchronously collected by each of the positioning devices to be measured during the movement of the mobile carrier; fusing the observation trajectory sequence to obtain positioning reference data; and comparing at least one observation trajectory sequence with the positioning reference data to obtain a positioning accuracy test result. Compared to traditional solutions that require the use of high-precision reference equipment with high purchase costs, the embodiment of the present application utilizes the fusion result of the trajectory data of at least two positioning devices to be measured as the positioning reference, rather than using the positioning data of a higher-precision positioning device as the positioning reference, thereby avoiding the additional purchase of dedicated reference equipment and significantly reducing the cost of positioning accuracy testing. This is particularly suitable for mass production verification scenarios of vehicle-mounted T-BOX devices that require large-scale deployment.

[0006] Optionally, the fusion processing of the observed trajectory sequence to obtain positioning reference data includes: performing time alignment processing on the first trajectory sequence and the second trajectory sequence to obtain a first target trajectory sequence; the timestamps of each trajectory point in the first target trajectory sequence and the second trajectory sequence correspond one-to-one; the first trajectory sequence and the second trajectory sequence both belong to the observed trajectory sequence; performing position transformation on the first target trajectory sequence according to affine transformation parameters to obtain a third trajectory sequence; the affine transformation parameters are obtained by minimizing the position difference between trajectory points with the same timestamp in the first target trajectory sequence and the second trajectory sequence; eliminating outliers in the target trajectory point set to obtain the positioning reference data; the target trajectory point set includes trajectory points in the second trajectory sequence and the third trajectory sequence. Compared to traditional solutions that require the use of high-precision reference equipment with high purchase costs, the embodiments of the present application use trajectory fusion of low-cost vehicle-mounted positioning equipment to generate a high-precision positioning reference, effectively avoiding the additional purchase of dedicated reference equipment and significantly reducing the cost of positioning accuracy testing. It is particularly suitable for mass production verification scenarios of vehicle-mounted T-BOX devices that require large-scale deployment.

[0007] Optionally, the position transformation of the first target trajectory sequence according to the affine transformation parameters to obtain the third trajectory sequence includes: for each first trajectory point in the first target trajectory sequence, determining a first weight value corresponding to the first trajectory point according to the confidence of the first trajectory point: the confidence is positively correlated with the first weight value; according to the first weight value, performing weighted summation on the distance parameters corresponding to the first trajectory points to obtain a loss value: the distance parameter is used to represent the distance between the first trajectory point and a second trajectory point with the same timestamp in the second trajectory sequence; and obtaining the affine transformation parameters by minimizing the loss value. Through time alignment processing, a correspondence between trajectory sequences collected by different devices is established. Based on this correspondence, affine transformation parameters are calculated by minimizing position differences. The affine transformation parameters are then applied to the first target trajectory sequence to generate a third trajectory sequence. This allows the trajectories of the two devices to be mapped to the same coordinate system, eliminating errors caused by differences in device installation positions. The trajectory points in the second and third trajectory sequences are merged into a target trajectory point set. Finally, the accuracy of the positioning data is improved by eliminating outliers in the target trajectory point set, thereby generating positioning reference data that meets positioning accuracy requirements at a relatively low cost.

[0008] Optionally, the elimination of outliers in the target trajectory point set to obtain the positioning reference data includes: fitting the trajectory points in the target trajectory point set to obtain a preliminary fitting curve; calculating the fitting residual corresponding to each trajectory point in the target trajectory point set based on the preliminary fitting curve; eliminating outliers in the target trajectory point set; the fitting residual is used to determine whether its corresponding trajectory point is an outlier; repeating the above steps until the remaining trajectory points in the target trajectory point set meet the preset accuracy requirements to obtain an optimized trajectory point set: fitting the trajectory points in the optimized trajectory point set to obtain a target fitting curve used as the positioning reference data. Thus, based on the dynamically updated fitting curve, a fitting residual is generated, and outliers with significant spatial position deviations are eliminated round by round, thereby gradually improving the consistency of the spatial distribution of the trajectory points until the preset accuracy requirements are met; finally, a target fitting curve is generated based on the optimized trajectory point set, significantly improving the accuracy and reliability of the positioning reference data.

[0009] Optionally, the time alignment processing of the first trajectory sequence and the second trajectory sequence to obtain the first target trajectory sequence includes: for each target timestamp in the target timestamp sequence, obtaining a first trajectory point set in the first trajectory sequence whose timestamp falls within a target time window: the target time window is a time period with the target timestamp as the center and a preset duration; based on a second weight value, performing weighted linear fitting on each trajectory point in the first trajectory point set to obtain a target fitting straight line; the second weight value corresponding to each trajectory point in the first trajectory point set is negatively correlated with the distance between the timestamp of the trajectory point and the target timestamp; according to the target fitting straight line, the trajectory point corresponding to the target timestamp is obtained to form the first target trajectory sequence. In this way, while achieving strict time alignment between the first trajectory sequence and the target timestamp sequence, the original trajectory is smoothed using weighted linear fitting, effectively suppressing random jitter of the trajectory point, which helps to improve the positioning accuracy of the trajectory point corresponding to the target timestamp.

[0010] Optionally, obtaining the observation trajectory sequence synchronously collected by each of the devices to be positioned during the movement of the mobile carrier includes: obtaining satellite positioning data collected by each of the devices to be positioned during the movement of the mobile carrier, as well as correction data obtained from a reference station; and using the correction data to correct each of the satellite positioning data to obtain the observation trajectory sequence. By fusing the observation trajectory sequence to construct positioning reference data, the application scenario is expanded to RTK positioning accuracy testing scenarios, and testing can be completed without the need for additional high-precision reference equipment.

[0011] Optionally, before fusing the observed trajectory sequences, the process further includes: obtaining motion state data collected by onboard sensors on the mobile carrier during the movement; and correcting each of the observed trajectory sequences based on the motion state data. This reduces errors and noise in the observed trajectory sequences, improves the accuracy of the observed trajectory sequences, and contributes to improving the accuracy of the positioning reference data.

[0012] Optionally, the device to be measured is a vehicle-mounted telematics terminal with real-time dynamic carrier phase differential positioning capabilities. By fusing the observed trajectory sequence to construct positioning reference data, the application scenario can be expanded to include testing scenarios for T-BOX devices with RTK capabilities, and testing can be completed without the need for additional high-precision reference equipment.

[0013] According to the second aspect of the present application, a positioning accuracy testing device is provided, including: an acquisition module for acquiring an observation trajectory sequence synchronously collected by each of the positioning devices to be measured during the movement of the mobile carrier; a fusion module for fusing the observation trajectory sequence to obtain positioning reference data; and a comparison module for comparing at least one observation trajectory sequence with the positioning reference data to obtain a positioning accuracy test result.

[0014] According to a third aspect of the present application, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor is configured to execute the method described in any one of the above embodiments.

[0015] According to a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above embodiments.

[0016] According to a fifth aspect of the present application, a positioning accuracy testing system is provided, comprising: the above-mentioned electronic device, and at least two positioning devices to be measured.

[0017] This application provides a positioning accuracy test method, device, and system. This solution includes: obtaining a sequence of observation trajectories synchronously collected by each of the positioning devices to be measured during the movement of the mobile carrier; fusing the observation trajectories to obtain positioning reference data; and comparing at least one observation trajectories sequence with the positioning reference data to obtain a positioning accuracy test result. Compared to traditional solutions that require the use of expensive high-precision reference equipment, the embodiments of this application utilize the trajectories of low-cost, vehicle-mounted positioning devices to fuse and generate a high-precision positioning reference, effectively avoiding the need to purchase additional dedicated reference equipment and significantly reducing the cost of positioning accuracy testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Shown is a schematic diagram of a mobile carrier provided in an embodiment of the present application.

[0019] Figure 2 The figure shows a flow chart of a positioning accuracy testing method provided in one embodiment of the present application.

[0020] Figure 3 Shown is a block diagram of a positioning accuracy testing device provided by an embodiment of the present application.

[0021] Figure 4 Shown is a structural block diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Application Overview

[0024] In the field of intelligent connected vehicles, the RTK (Real-Time Kinematic) technology integrated into the T-BOX (Telematics Box) device receives carrier phase differential data broadcast by base stations and performs real-time corrections for ionospheric delay, tropospheric delay, and satellite orbit errors in global navigation satellite system signals. This improves positioning accuracy from meters to centimeters, significantly enhancing the accuracy and reliability of the positioning system. This technological breakthrough provides core positioning support for applications such as advanced driver assistance systems, high-precision map acquisition, and autonomous driving.

[0025] In practical applications, when testing the positioning accuracy of a T-BOX device with RTK functionality, traditional solutions require the use of a higher-precision positioning device as a reference device to ensure reliability. The position data collected by the reference device is considered the actual position (i.e., the true value), and the position data collected by the T-BOX device under test is used as the observed value. This value is then compared with the position data collected by the reference device to obtain the positioning accuracy test result of the T-BOX device. Therefore, the positioning accuracy test result of the T-BOX device inevitably includes the positioning error of the reference device. If the reference device is not accurate enough, the test result will be unreliable.

[0026] However, the positioning accuracy of RTK-enabled T-BOX devices is relatively high, typically reaching the centimeter level. This requires that the positioning accuracy of the reference device must be even higher (e.g., millimeter level or better). As a result, the acquisition, maintenance, and system integration costs of the reference device are much higher than those of the vehicle-mounted T-BOX device, and the positioning accuracy testing cost is high. Especially in large-scale mass production testing or long-term road testing scenarios, the high cost investment makes this verification method difficult for automakers to accept in commercial applications.

[0027] In order to solve the above problems, the embodiment of the present application obtains an observation trajectory sequence synchronously collected by at least two positioning devices to be tested (i.e., T-BOX devices) during the movement of the mobile carrier; fuses the observation trajectory sequence, and uses the fusion result of the two as positioning reference data (i.e., true value); compares at least one observation trajectory sequence with the positioning reference data to obtain a positioning accuracy test result. The fusion result of the trajectory data of at least two T-BOX devices to be tested is used as the positioning reference, rather than using the positioning data of a higher-precision positioning device as the positioning reference, thereby avoiding dependence on high-precision reference equipment, effectively reducing the cost of positioning accuracy testing, and improving the feasibility and economy of large-scale mass production testing and long-term road test scenarios.

[0028] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0029] Figure 1 FIG. 1 is a schematic diagram of a mobile carrier provided in an embodiment of the present application. Figure 1 As shown, two antennas, ANT1 and ANT2, are mounted on mobile carrier 2. These two antennas are located on top of mobile carrier 2, arranged front to back along the central axis. Both the first device and the second device are positioning devices installed on the mobile carrier to be tested, and can obtain satellite positioning signals via the ANT1 and ANT2 antennas, respectively. The first device and the second device can be configured as telematics boxes (T-BOXs) with integrated satellite positioning capabilities. Both the first device and the second device can obtain differential correction data, such as satellite orbit errors and ionospheric delay corrections, from reference station 1.

[0030] Exemplary Methods

[0031] Figure 2 It is a flowchart of a positioning accuracy testing method provided in one embodiment of the present application. Figure 2The method described is performed by a computing device, but the embodiments of the present application are not limited thereto. The computing device may be an in-vehicle computing device for online computing, or a server for offline computing. The server may be a single server, or comprised of multiple servers, or a virtualization platform, or a cloud computing service center, and the embodiments of the present application are not limited thereto.

[0032] like Figure 2 As shown, the method includes the following contents:

[0033] Step S210: obtaining an observation trajectory sequence synchronously collected by each of the devices to be positioned during the movement of the mobile carrier.

[0034] In the embodiment of the present application, the mobile carrier is used to carry the device to be positioned and move, and can be a vehicle, a drone, a robot, etc., and there is no specific limitation on this.

[0035] In the embodiments of the present application, the positioning devices to be measured are positioning devices to be measured that are installed on a mobile carrier, such as satellite positioning devices, real-time kinematic carrier phase differential technology (RTK) devices, and ultra-wideband positioning devices. The positioning devices to be measured may also include an inertial measurement unit (IMU).

[0036] In the embodiment of the present application, the positioning device to be measured is preferably a device with similar nominal accuracy, and can usually be a low-cost device of the same model. In the embodiment of the present application, the positioning device to be measured can be a positioning device of the same model.

[0037] In the embodiment of the present application, each of the observation trajectory sequences is synchronously collected by each of the devices to be positioned; the observation trajectory sequence may be a sequence consisting of trajectory points.

[0038] Step S220: performing fusion processing on the observation trajectory sequence to obtain positioning reference data.

[0039] In the embodiment of the present application, the positioning reference data may refer to trajectory data obtained by optimizing the observation trajectory sequence through a fusion algorithm; and its accuracy is higher than that of the observation trajectory sequence.

[0040] In the embodiment of the present application, the fusion processing of the observation trajectory sequence can be achieved through algorithms such as weighted averaging and Kalman filtering, and there is no specific limitation on this.

[0041] Step S230: Compare at least one observation trajectory sequence with the positioning reference data to obtain a positioning accuracy test result.

[0042] In the embodiment of the present application, the positioning accuracy test results may include statistical values ​​such as the average value, maximum value, standard deviation, etc. of the horizontal positioning accuracy and the vertical positioning accuracy, and there is no specific limitation on this.

[0043] In an embodiment of the present application, the positioning accuracy test result can be obtained by comparing the observation trajectory sequence with the positioning reference data. Specifically, the spatial coordinate difference between each positioning point in the observation trajectory sequence and the reference positioning point at the corresponding moment in the positioning reference data is calculated to obtain the coordinate deviation in the horizontal direction (such as the latitude and longitude plane) and the vertical direction (such as the elevation direction). Based on these deviation data, a statistical analysis method is used to calculate the average values ​​of the horizontal positioning accuracy and the vertical positioning accuracy, respectively, to reflect the average deviation degree of the overall positioning; the maximum value is calculated to characterize the maximum positioning error that may occur; the standard deviation is calculated to measure the discrete degree of the positioning deviation, and then the positioning accuracy test result containing the above statistical values ​​is obtained.

[0044] In an embodiment of the present application, an observation trajectory sequence synchronously collected by each of the devices to be positioned is obtained during the movement of the mobile carrier; the observation trajectory sequence is fused to obtain positioning reference data; at least one observation trajectory sequence is compared with the positioning reference data to obtain a positioning accuracy test result. At least one observation trajectory sequence is compared with the positioning reference data to obtain a positioning accuracy test result. Compared with the traditional solution that must use high-precision reference equipment with a high purchase cost, the embodiment of the present application uses the fusion result of the trajectory data of at least two devices to be positioned as the positioning reference, rather than using the positioning data of a higher-precision positioning device as the positioning reference, thereby avoiding the additional purchase of dedicated reference equipment and significantly reducing the cost of positioning accuracy testing. It is particularly suitable for mass production verification scenarios of vehicle-mounted T-BOX devices that require large-scale deployment.

[0045] based on Figure 2 The method in this specification also provides some specific implementation plans of the method, which are described below.

[0046] Optionally, the fusing the observation trajectory sequence to obtain positioning reference data includes:

[0047] Performing time alignment processing on the first trajectory sequence and the second trajectory sequence to obtain a first target trajectory sequence; in the first target trajectory sequence and the second trajectory sequence, the timestamps of the trajectory points correspond one to one; the first trajectory sequence and the second trajectory sequence both belong to the observation trajectory sequence;

[0048] Performing a position transformation on the first trajectory sequence according to an affine transformation parameter to obtain a third trajectory sequence; the affine transformation parameter is obtained by minimizing the position difference between trajectory points with the same timestamp in the first target trajectory sequence and the second trajectory sequence;

[0049] Outliers in a target trajectory point set are eliminated to obtain the positioning reference data; the target trajectory point set includes trajectory points in the second trajectory sequence and the third trajectory sequence.

[0050] In an embodiment of the present application, the first trajectory sequence and the second trajectory sequence are both observation trajectory sequences collected by the device to be positioned; the timestamps of the trajectory points in the second trajectory sequence are used as a reference for time alignment, and the second device that collects the second trajectory sequence can be installed directly above the center of the rear wheel of the target carrier; the first trajectory sequence can be an observation trajectory sequence collected by other devices to be positioned (i.e., the first device). In order to reduce errors caused by different installation positions, the first device can be installed as close to the second device as possible. Preferably, the first device and the second device are both installed on the central axis of the top of the target carrier.

[0051] It should be noted that the first trajectory sequence can refer to observation trajectory sequences in addition to the second trajectory sequence. The number of such sequences is not limited to one; it depends on the number of devices to be measured. For simplicity, the following description uses a single first trajectory sequence. In actual testing, if multiple devices to be measured are operating simultaneously, the observation trajectory output by each device can be treated as an independent first trajectory sequence and subjected to processing steps such as time alignment and position transformation to obtain third trajectory sequences corresponding to each first trajectory sequence. The target trajectory point set is then derived from these third trajectory sequences and the second trajectory sequence.

[0052] In the embodiment of the present application, the time alignment process is used to map the trajectory points with inconsistent timestamps in the first trajectory sequence and the second trajectory sequence to a unified timestamp sequence, thereby ensuring that the timestamps of the first target trajectory sequence and the second trajectory sequence are strictly aligned.

[0053] In an embodiment of the present application, the affine transformation parameters include translation parameters, rotation parameters and scale parameters, which are calculated by the least squares method or the iterative closest point (ICP) algorithm and are used to eliminate the distance between the first device and the second device.

[0054] In an embodiment of the present application, the position transformation may refer to translating, rotating, and scaling each trajectory point in the first target trajectory sequence according to the affine transformation parameters, so that the transformed trajectory point overlaps with the corresponding point in the second trajectory sequence to the greatest extent in spatial position.

[0055] In the embodiment of the present application, the third trajectory sequence may be the first trajectory sequence after time alignment and position transformation, and may be regarded as a trajectory point sequence of the second device collected by the first device.

[0056] In the embodiment of the present application, the outlier point may refer to a trajectory point in the target trajectory point set whose position error exceeds a preset threshold.

[0057] In an embodiment of the present application, the positioning reference data may be a set of trajectory points after outliers are removed from the target trajectory point set; or it may be a trajectory curve obtained by fitting the remaining trajectory points after outliers are removed.

[0058] In an embodiment of the present application, time alignment is performed on the first trajectory sequence and the second trajectory sequence to generate a first target trajectory sequence, ensuring a strict one-to-one correspondence between the timestamps of each trajectory point in the first target trajectory sequence and the second trajectory sequence. Affine transformation parameters are calculated by minimizing the position difference between trajectory points with the same timestamp in the first target trajectory sequence and the second trajectory sequence. The first target trajectory sequence is positionally transformed based on the affine transformation parameters to generate a third trajectory sequence. The trajectory points in the second and third trajectory sequences are merged into a target trajectory point set; outliers that deviate from the expected distribution in the target trajectory point set are eliminated to generate the positioning reference data.

[0059] In an embodiment of the present application, a correspondence between trajectory sequences collected by different devices is established through time alignment processing. Based on this correspondence, affine transformation parameters are calculated by minimizing position differences, and then the affine transformation parameters are applied to perform position transformation on the first target trajectory sequence to generate a third trajectory sequence, so that the trajectories of the two devices are mapped to the same coordinate system, eliminating errors caused by differences in device installation positions; the trajectory points in the second trajectory sequence and the third trajectory sequence are merged into a target trajectory point set; finally, the accuracy of the positioning data is improved by eliminating outliers in the target trajectory point set, thereby generating positioning reference data that meets positioning accuracy requirements at a lower cost.

[0060] In another embodiment, a first trajectory sequence and a second trajectory sequence are time-aligned to obtain a first target trajectory sequence and a second target trajectory sequence, wherein the timestamps of the trajectory points in the first target trajectory sequence and the second target trajectory sequence correspond one to one; the first target trajectory sequence is positionally transformed according to affine transformation parameters to obtain a third trajectory sequence; the affine transformation parameters are obtained by minimizing the position difference between trajectory points with the same timestamp in the first target trajectory sequence and the second target trajectory sequence; outliers in the target trajectory point set are eliminated to obtain the positioning reference data; the target trajectory point set includes the trajectory points in the second target trajectory sequence and the third trajectory sequence.

[0061] Optionally, performing position transformation on the first target trajectory sequence according to the affine transformation parameters to obtain a third trajectory sequence includes:

[0062] For each first trajectory point in the first target trajectory sequence, determining a first weight value corresponding to the first trajectory point according to a confidence level of the first trajectory point, where the confidence level is positively correlated with the first weight value;

[0063] performing a weighted summation of distance parameters corresponding to the first trajectory points according to the first weight value to obtain a loss value, wherein the distance parameter is used to represent the distance between the first trajectory point and a second trajectory point having the same timestamp in the second trajectory sequence;

[0064] The affine transformation parameters are obtained by minimizing the loss value.

[0065] In the embodiment of the present application, the first trajectory point refers to a trajectory point in the first target trajectory sequence, including information such as position coordinates and timestamp.

[0066] In an embodiment of the present application, the first weight value is a value determined based on the confidence of the first trajectory point, which is used to measure the importance of the corresponding first trajectory point in calculating the loss value. The higher the confidence, the greater the first weight value.

[0067] In the embodiment of the present application, the confidence level is used to indicate the reliability of the first trajectory point; specifically, the confidence level can be determined based on parameters such as the number of satellites, signal-to-noise ratio, and acquisition scenario (urban / mountainous / open area).

[0068] In the embodiment of the present application, the confidence C calculation formula may be:

[0069]

[0070] Where, P objIt is used to indicate the probability of target existence. The default value of vehicle positioning scenario is 1. IoU is the intersection over union ratio between the positioning point and the true trajectory, which needs to be calculated by combining historical trajectory data fitting. sat is the number of satellites; SNR is the signal-to-noise ratio; SNR max and N max They represent the maximum signal-to-noise ratio and the maximum number of satellites, respectively, and can be adaptively adjusted according to the scenario; λ is the environmental interference compensation coefficient, and a lower value can be used in complex areas such as urban canyons.

[0071] In the embodiment of the present application, the loss value L can be calculated by the following loss function:

[0072]

[0073] Where, are the horizontal and vertical coordinates of the i-th first trajectory point; w is the horizontal and vertical coordinates of the second track point with the same timestamp as the first track point in the second track sequence; i is the first weight value corresponding to the i-th first trajectory point; a, b, c, d, e, and f are all affine transformation parameters, where a and d can be used to control the scaling and shearing transformation in the x and y directions, respectively. It can be used to represent the position coordinates of the second trajectory point after affine transformation.

[0074] In some cases, in order to prevent the model from overfitting during training, a regularization term can be introduced into the loss function to constrain the size of the affine transformation parameters. The regularization term can be L1 regularization term and / or L2 regularization term. When using L2 regularization term, α(a 2 +b 2 +d 2 +e 2 )+β(c 2 +f 2 ), where α and β are regularization coefficients, where α controls the constraint strength of the rotation and scaling parameters, and β controls the constraint strength of the translation parameters.

[0075] In an embodiment of the present application, minimizing the loss value L is taken as the optimization goal, and the affine transformation parameters a, b, c, d, e, and f are iteratively solved through optimization algorithms such as the least squares method and the gradient descent method to obtain the optimal affine transformation parameter combination.

[0076] In this embodiment of the present application, a first weight value corresponding to the first trajectory point is determined based on its confidence level. Furthermore, based on the first weight value, a weighted sum of the distance parameters corresponding to the first trajectory point is performed to obtain a loss value: the distance parameter represents the distance between the first trajectory point and a second trajectory point with the same timestamp in the second trajectory sequence. The affine transformation parameters are obtained by minimizing the loss value. By associating the confidence level of the first trajectory point with the first weight value, high-confidence trajectory points are assigned greater weights, causing them to dominate the optimization direction of the affine transformation parameters in the loss function. Low-confidence trajectory points are assigned smaller weights, effectively suppressing the interference of low-confidence trajectory points on the affine transformation calculation, thereby effectively improving the accuracy of the affine transformation parameter calculation.

[0077] Optionally, removing outliers from the target trajectory point set to obtain the positioning reference data includes:

[0078] Performing fitting processing on the trajectory points in the target trajectory point set to obtain a preliminary fitting curve;

[0079] Calculating the fitting residual corresponding to each trajectory point in the target trajectory point set according to the preliminary fitting curve;

[0080] Eliminate outliers in the target trajectory point set; the fitting residual is used to determine whether the corresponding trajectory point is an outlier;

[0081] Repeat the above steps until the remaining trajectory points in the target trajectory point set meet the preset accuracy requirements, and obtain the optimized trajectory point set:

[0082] The trajectory points in the optimized trajectory point set are fitted to obtain a target fitting curve used as the positioning reference data.

[0083] In an embodiment of the present application, the preliminary fitting curve is a curve obtained by preliminary fitting of the target trajectory point set, which can be generated using algorithms such as least squares method, polynomial fitting or spline interpolation, and is used to characterize the overall distribution trend of the trajectory points.

[0084] In the embodiment of the present application, the fitting residual is the Euclidean distance between the actual coordinates of the trajectory point and the predicted coordinates of the preliminary fitting curve, which is used to quantify the degree of deviation between the trajectory point and the fitting curve.

[0085] In an embodiment of the present application, the outlier refers to a trajectory point whose fitting residual exceeds a preset threshold, and the preset threshold is dynamically set based on the statistical characteristics of the residual, including but not limited to using the 3σ principle or the interquartile range method to determine the threshold boundary; specifically, under the normal distribution assumption, the threshold is set to 3 times the standard deviation of the residual mean, or the interquartile range method is used to set the threshold boundary to (Q1-1.5IQR, Q3+1.5IQR), where Q1 is the first quartile, Q3 is the third quartile, IQR is the interquartile range, and IQR=Q3-Q1.

[0086] In an embodiment of the present application, the preset accuracy requirement refers to the iteration termination condition, for example, the fitting residuals of all remaining trajectory points are less than a set threshold, or the curve smoothness index obtained by fitting the remaining trajectory points reaches a preset standard, or the number of iterations reaches a preset upper limit.

[0087] In the embodiment of the present application, the optimized trajectory point set is a trajectory point set that meets the preset accuracy requirement after removing outliers.

[0088] In an embodiment of the present application, the positioning reference data may include a trajectory curve generated by fitting the optimized trajectory point set.

[0089] In the embodiment of the present application, the preliminary fitting curve and the positioning reference data adopt the same or different fitting algorithms; the positioning reference data can be implemented using a fitting algorithm with higher accuracy.

[0090] In an embodiment of the present application, the trajectory points in the target trajectory point set are fitted to obtain a preliminary fitting curve; based on the preliminary fitting curve, the fitting residual corresponding to each trajectory point in the target trajectory point set is calculated; outliers in the target trajectory point set are eliminated; the fitting residual is used to determine whether the corresponding trajectory point is an outlier; the above steps are repeated until the remaining trajectory points in the target trajectory point set meet the preset accuracy requirements, thereby obtaining an optimized trajectory point set: the trajectory points in the optimized trajectory point set are fitted to obtain a target fitting curve used as the positioning reference data. Thus, a fitting residual is generated based on the dynamically updated fitting curve, and outliers with significantly deviated spatial positions are eliminated round by round, thereby gradually improving the spatial distribution consistency of the trajectory points until the preset accuracy requirements are met; finally, a target fitting curve is generated based on the optimized trajectory point set, significantly improving the accuracy and reliability of the positioning reference data.

[0091] Optionally, performing time alignment processing on the first trajectory sequence and the second trajectory sequence to obtain a first target trajectory sequence includes:

[0092] For each target timestamp in the target timestamp sequence, obtain a first trajectory point set in the first trajectory sequence whose timestamp falls within a target time window: the target time window is a time period of a preset length centered on the target timestamp;

[0093] Based on the second weight value, a weighted linear fit is performed on each trajectory point in the first trajectory point set to obtain a target fitting line; the second weight value corresponding to each trajectory point in the first trajectory point set is negatively correlated with the distance between the timestamp of the trajectory point and the target timestamp;

[0094] According to the target fitting straight line, trajectory points corresponding to the target timestamp are obtained to form a first target trajectory sequence.

[0095] In this embodiment of the present application, the target timestamp sequence is used as a time reference to unify the first and second trajectory sequences. The target timestamp sequence can be a set of timestamps in the second trajectory sequence, or a sequence of equally spaced timestamps generated at a preset frequency. When an equally spaced time sequence is used, the second trajectory sequence must also be synchronized to it.

[0096] In the embodiment of the present application, the target time window is a time period of preset duration centered on the target timestamp, and is used to define the range of trajectory points involved in fitting.

[0097] In an embodiment of the present application, the preset duration can be set according to the sampling frequency of the trajectory points.

[0098] In an embodiment of the present application, the first trajectory point set is a set of all trajectory points in the first trajectory sequence whose timestamps fall within the target time window.

[0099] In the embodiment of the present application, the second weight value decays as the absolute difference between the trajectory point timestamp and the target timestamp increases, and can be calculated using a Gaussian weighting function.

[0100] Specifically, for the target timestamp t0, the first trajectory sequence within the target time window is extracted, and each trajectory point in the first trajectory point set can be expressed as (t j ,z j ), where t j is the original timestamp, z j is the position coordinate of the corresponding trajectory point; calculate the time interval Δt between adjacent original timestamps i =t j+1 ―t j , get the time interval value Δt j The standard deviation δ can be used to indicate the temporal distribution density of the original trajectory points within the target time window.

[0101] The second weight value is Where t0 is the target timestamp, t j is the original timestamp, δ is the time interval Δt between adjacent original timestamps j The standard deviation of .

[0102] In the embodiment of the present application, the weighted linear fitting refers to performing least squares straight line fitting on the first trajectory point set based on the second weight value, so that the fitting straight line approaches each trajectory point.

[0103] Specifically, the fitting formula is Where m is the slope, which can be used to represent the rate of change of position over time, and c is the intercept, which can be used to represent the initial position offset. By minimizing the weighted residual sum of squares objective function: m and c can be obtained by minimizing the weighted residual sum of squares, that is, Where, In an embodiment of the present application, the target fitting straight line is a straight line equation obtained by weighted linear fitting, which is used to describe the temporal trend of the trajectory points in the target time window.

[0104] In an embodiment of the present application, the trajectory point corresponding to the target timestamp can be calculated by substituting the target timestamp into the target fitting straight line equation.

[0105] In an embodiment of the present application, the first target trajectory sequence is a sequence composed of trajectory points corresponding to all target timestamps in chronological order, and its timestamps are strictly aligned with the target timestamp sequence.

[0106] In an embodiment of the present application, for each target timestamp in the target timestamp sequence, a first set of trajectory points in the first trajectory sequence whose timestamps fall within the target time window is obtained: based on a second weight value, a weighted linear fit is performed on each trajectory point in the first trajectory point set to obtain a target fitting line; based on the target fitting line, the trajectory points corresponding to the target timestamp are obtained to form a first target trajectory sequence. This achieves strict time alignment between the first trajectory sequence and the target timestamp sequence while using weighted linear fitting to smooth the original trajectory, effectively suppressing random jitter of the trajectory points and helping to improve the positioning accuracy of the trajectory points corresponding to the target timestamps.

[0107] Optionally, the acquiring of the observation trajectory sequence synchronously collected by each of the devices to be positioned during the movement of the mobile carrier includes:

[0108] Acquiring satellite positioning data collected by each of the positioning devices to be measured during the movement of the mobile carrier, as well as correction data obtained from a reference station;

[0109] The correction data is used to correct each of the satellite positioning data to obtain the observation trajectory sequence.

[0110] In the embodiment of the present application, the positioning devices to be measured are all dynamic carrier phase differential positioning devices (Real-Time Kinematic, RTK), and can also be positioning devices with RTK functions, such as vehicle-mounted telematics terminals.

[0111] In an embodiment of the present application, the satellite positioning data may refer to positioning-related data obtained from a satellite, which may include original carrier phase observations, pseudorange observations, satellite ephemeris parameters, and original timestamps generated by a built-in clock of the device.

[0112] In the embodiment of the present application, the reference station is a satellite signal receiving station fixedly set at a precisely known coordinate point, which continuously receives satellite signals and generates differential correction data, and broadcasts it to the mobile terminal through a wireless communication link.

[0113] In the embodiment of the present application, the correction data is the differential positioning data broadcast in real time by the reference station through a wireless communication link, which is used to eliminate the common error terms in the observation data collected by the positioning device to be measured.

[0114] In an embodiment of the present application, the observation trajectory sequence is an ordered set of positioning points generated by the positioning device to be measured after differential processing of the original satellite positioning data based on the correction data. Each positioning point contains a high-precision timestamp and corresponding three-dimensional coordinates (longitude, latitude, elevation).

[0115] In the embodiment of the present application, the use of the correction data to correct each satellite positioning data separately may refer to eliminating systematic error items such as ionospheric propagation delay, tropospheric refraction delay and satellite orbit error through carrier phase difference technology.

[0116] The positioning devices to be measured are all RTK positioning devices. Traditional positioning accuracy testing methods generally use positioning devices with higher accuracy as external reference devices, resulting in higher testing costs.

[0117] In an embodiment of the present application, satellite positioning data collected by each of the devices to be measured during the movement of the mobile carrier, as well as correction data obtained from the reference station, are obtained. The correction data is used to correct each of the satellite positioning data to obtain the observation trajectory sequence. The observation trajectory sequence is fused to construct positioning reference data, thereby expanding the application scenario to RTK positioning accuracy testing scenarios with two devices acting as a reference, and the test can be completed without the need for additional high-precision reference equipment.

[0118] Optionally, before fusing the observation trajectory sequence, the method further includes:

[0119] Acquiring motion state data collected by a vehicle-mounted sensor at the mobile carrier during the movement;

[0120] Based on the motion state data, each of the observation trajectory sequences is corrected respectively.

[0121] In the embodiment of the present application, the vehicle-mounted sensor may refer to a sensor installed on the mobile carrier for monitoring the motion state, such as an inertial measurement unit, a wheel speed sensor, a steering wheel angle sensor, etc.

[0122] In an embodiment of the present application, the motion state data may refer to the data collected by the vehicle-mounted sensors; for example, the three-axis angular velocity and three-axis acceleration output by the inertial measurement unit, the vehicle speed output by the wheel speed sensor, and the steering angle value output by the steering wheel angle sensor.

[0123] In the embodiment of the present application, the correction of the first initial trajectory sequence and the second initial trajectory sequence may be performed by using algorithms such as Kalman filtering and particle filtering, and there is no specific limitation on this.

[0124] In an embodiment of the present application, before the observation trajectory sequence is fused, each of the observation trajectory sequences is corrected based on the motion state data collected by the vehicle-mounted sensor during the movement, thereby reducing the error and noise of the observation trajectory sequence, improving the accuracy of the observation trajectory sequence, and helping to improve the accuracy of the positioning reference data.

[0125] Optionally, the device to be positioned is a vehicle-mounted telematics terminal with a real-time dynamic carrier phase differential positioning function.

[0126] In the embodiment of the present application, the real-time dynamic carrier phase differential positioning function is the RTK (Real Time Kinematic) function.

[0127] In the embodiment of the present application, the vehicle-mounted telematics terminal is a vehicle-mounted terminal that supports RTK positioning and wireless data communication.

[0128] In this embodiment, the devices to be measured are all T-BOX devices with RTK positioning capabilities. Traditional methods rely on more accurate positioning devices as external reference devices. By fusing the observed trajectory sequences to construct positioning reference data, the application scenario is expanded to test T-BOX devices with RTK capabilities, and testing can be completed without the need for additional high-precision reference devices.

[0129] Exemplary devices

[0130] The device embodiments of this application can be used to execute the method embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.

[0131] Figure 3 The figure shows a block diagram of a positioning accuracy test device provided by an embodiment of the present application. Figure 3 As shown, the device 300 includes:

[0132] An acquisition module 310 is configured to acquire an observation trajectory sequence synchronously collected by each of the devices to be positioned during movement of the mobile carrier;

[0133] A fusion module 320 is used to perform fusion processing on the observation trajectory sequence to obtain positioning reference data;

[0134] The comparison module 330 is used to compare at least one observation trajectory sequence with the positioning reference data to obtain a positioning accuracy test result.

[0135] Optionally, the fusion module 320 includes:

[0136] a time alignment unit, configured to perform time alignment processing on the first trajectory sequence and the second trajectory sequence to obtain a first target trajectory sequence; wherein the timestamps of the trajectory points in the first target trajectory sequence and the second trajectory sequence correspond one to one; and the first trajectory sequence and the second trajectory sequence both belong to the observation trajectory sequence;

[0137] a position transformation unit, configured to perform position transformation on the first target trajectory sequence according to affine transformation parameters to obtain a third trajectory sequence; the affine transformation parameters are obtained by minimizing the position difference between trajectory points with the same timestamp in the first target trajectory sequence and the second trajectory sequence;

[0138] The reference generation unit is used to eliminate outliers in a target trajectory point set to obtain the positioning reference data; the target trajectory point set includes trajectory points in the second trajectory sequence and the third trajectory sequence.

[0139] Optionally, the position transformation unit is configured to:

[0140] For each first trajectory point in the first target trajectory sequence, determining a first weight value corresponding to the first trajectory point according to a confidence level of the first trajectory point, where the confidence level is positively correlated with the first weight value;

[0141] performing a weighted summation of distance parameters corresponding to the first trajectory points according to the first weight value to obtain a loss value, wherein the distance parameter is used to represent the distance between the first trajectory point and a second trajectory point having the same timestamp in the second trajectory sequence;

[0142] The affine transformation parameters are obtained by minimizing the loss value.

[0143] Optionally, the benchmark generating unit is configured to:

[0144] Performing fitting processing on the trajectory points in the target trajectory point set to obtain a preliminary fitting curve;

[0145] Calculating the fitting residual corresponding to each trajectory point in the target trajectory point set according to the preliminary fitting curve;

[0146] Eliminate outliers in the target trajectory point set; the fitting residual is used to determine whether the corresponding trajectory point is an outlier;

[0147] Repeat the above steps until the remaining trajectory points in the target trajectory point set meet the preset accuracy requirements, and obtain the optimized trajectory point set:

[0148] The trajectory points in the optimized trajectory point set are fitted to obtain a target fitting curve used as the positioning reference data.

[0149] Optionally, the time alignment unit is configured to:

[0150] For each target timestamp in the target timestamp sequence, obtain a first trajectory point set in the first trajectory sequence whose timestamp falls within a target time window: the target time window is a time period of a preset length centered on the target timestamp;

[0151] Based on the second weight value, a weighted linear fit is performed on each trajectory point in the first trajectory point set to obtain a target fitting line; the second weight value corresponding to each trajectory point in the first trajectory point set is negatively correlated with the distance between the timestamp of the trajectory point and the target timestamp;

[0152] According to the target fitting straight line, trajectory points corresponding to the target timestamp are obtained to form a first target trajectory sequence.

[0153] Optionally, the acquisition module 310 is configured to:

[0154] Acquiring satellite positioning data collected by each of the positioning devices to be measured during the movement of the mobile carrier, as well as correction data obtained from a reference station;

[0155] The correction data is used to correct each of the satellite positioning data to obtain the observation trajectory sequence.

[0156] Optionally, the acquisition module 310 is configured to acquire motion state data collected by an onboard sensor at the mobile carrier during the movement;

[0157] The apparatus 300 further includes: a correction unit, configured to correct each of the observation trajectory sequences based on the motion state data.

[0158] Optionally, the device to be positioned is a vehicle-mounted telematics terminal with a real-time dynamic carrier phase differential positioning function.

[0159] Exemplary electronic devices

[0160] Below, reference Figure 4 To describe the electronic device according to the embodiment of the present application. Figure 4 The figure shows a block diagram of an electronic device according to an embodiment of the present application.

[0161] like Figure 4 As shown, electronic device 400 includes one or more processors 410 and memory 420 .

[0162] The processor 410 may have other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0163] Specifically, the processor 410 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the solution of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The processor 410 may also include a main processor, a baseband chip, a modem, etc.

[0164] The memory 420 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 410 may execute the program instructions to implement the positioning accuracy test method of each embodiment of the present application described above and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0165] In one example, the electronic device 400 may further include an input device 430 and an output device 440 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0166] In addition, the input device 430 may also be a device that receives data and information input by the user, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor. The output device 440 may output various information to the outside. The output device 440 may include, for example, a display, speaker, printer, communication network and its connected remote output device, etc.

[0167] Of course, to simplify, Figure 4 Only some of the components related to the present application in the electronic device 400 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device 400 may further include any other appropriate components according to specific application scenarios.

[0168] Exemplary Systems

[0169] In addition to the above-mentioned method and device, an embodiment of the present application may also be a positioning accuracy testing system, which may include the above-mentioned electronic device and at least two positioning devices to be measured.

[0170] Exemplary computer program products and computer-readable storage media

[0171] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the positioning accuracy testing method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0172] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0173] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the positioning accuracy testing method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0174] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0175] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0176] For the sake of simplicity, the aforementioned method embodiments are described as a series of action combinations. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0177] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similarities between the various embodiments can be referred to in conjunction with each other. For device embodiments, since they are generally similar to method embodiments, their description is relatively simple, and for relevant details, reference can be made to the description of the method embodiments.

[0178] The steps in the methods of each embodiment of the present application can be adjusted in sequence, merged, and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0179] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0180] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0181] The modules or submodules described as separate components may or may not be physically separate, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules may be selected to achieve the purpose of this embodiment according to actual needs.

[0182] In addition, each functional module or submodule in each embodiment of the present application may be integrated into a processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into a single module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or software functional modules or submodules.

[0183] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0184] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, software units executed by a processor, or a combination of the two. The software units may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0185] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0186] The above description of the disclosed embodiments will enable those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. A positioning accuracy testing method, characterized in that: At least two devices to be positioned are provided on a mobile carrier; the method comprises: Acquire a sequence of observation trajectories synchronously collected by each of the devices to be positioned during movement of the mobile carrier; Performing fusion processing on the observation trajectory sequence to obtain positioning reference data; Compare at least one observation trajectory sequence with the positioning reference data to obtain a positioning accuracy test result.

2. The method according to claim 1, characterized in that The fusing process of the observation trajectory sequence to obtain positioning reference data includes: Performing time alignment processing on the first trajectory sequence and the second trajectory sequence to obtain a first target trajectory sequence; in the first target trajectory sequence and the second trajectory sequence, the timestamps of the trajectory points correspond one to one; the first trajectory sequence and the second trajectory sequence both belong to the observation trajectory sequence; Performing a position transformation on the first target trajectory sequence according to affine transformation parameters to obtain a third trajectory sequence; the affine transformation parameters are obtained by minimizing the position difference between trajectory points with the same timestamp in the first target trajectory sequence and the second trajectory sequence; Outliers in a target trajectory point set are eliminated to obtain the positioning reference data; the target trajectory point set includes trajectory points in the second trajectory sequence and the third trajectory sequence.

3. The method according to claim 2, characterized in that The step of performing position transformation on the first target trajectory sequence according to the affine transformation parameters to obtain a third trajectory sequence includes: For each first trajectory point in the first target trajectory sequence, determining a first weight value corresponding to the first trajectory point according to a confidence level of the first trajectory point, where the confidence level is positively correlated with the first weight value; performing a weighted summation of distance parameters corresponding to the first trajectory points according to the first weight value to obtain a loss value, wherein the distance parameter is used to represent the distance between the first trajectory point and a second trajectory point having the same timestamp in the second trajectory sequence; The affine transformation parameters are obtained by minimizing the loss value.

4. The method according to claim 2, characterized in that The step of removing outliers from the target trajectory point set to obtain the positioning reference data includes: Performing fitting processing on the trajectory points in the target trajectory point set to obtain a preliminary fitting curve; Calculating the fitting residual corresponding to each trajectory point in the target trajectory point set according to the preliminary fitting curve; Eliminate outliers in the target trajectory point set; the fitting residual is used to determine whether the corresponding trajectory point is an outlier; Repeat the above steps until the remaining trajectory points in the target trajectory point set meet the preset accuracy requirements, and obtain the optimized trajectory point set: The trajectory points in the optimized trajectory point set are fitted to obtain a target fitting curve used as the positioning reference data.

5. The method according to claim 2, characterized in that The performing time alignment processing on the first trajectory sequence and the second trajectory sequence to obtain a first target trajectory sequence includes: For each target timestamp in the target timestamp sequence, obtain a first trajectory point set in the first trajectory sequence whose timestamp falls within a target time window: the target time window is a time period of a preset length centered on the target timestamp; Based on the second weight value, a weighted linear fit is performed on each trajectory point in the first trajectory point set to obtain a target fitting line; the second weight value corresponding to each trajectory point in the first trajectory point set is negatively correlated with the distance between the timestamp of the trajectory point and the target timestamp; According to the target fitting straight line, trajectory points corresponding to the target timestamp are obtained to form a first target trajectory sequence.

6. The method according to claim 1, characterized in that The acquiring of the observation trajectory sequence synchronously collected by each of the devices to be positioned during the movement of the mobile carrier includes: Acquiring satellite positioning data collected by each of the positioning devices to be measured during the movement of the mobile carrier, as well as correction data obtained from a reference station; The correction data is used to correct each of the satellite positioning data to obtain the observation trajectory sequence.

7. The method according to claim 1, characterized in that Before fusing the observation trajectory sequence to obtain positioning reference data, the method further includes: Acquiring motion state data collected by a vehicle-mounted sensor at the mobile carrier during the movement; Based on the motion state data, each of the observation trajectory sequences is corrected respectively.

8. The method according to any one of claims 1 to 7, characterized in that The device to be positioned is a vehicle-mounted telematics terminal with a real-time dynamic carrier phase differential positioning function.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the method according to any one of claims 1 to 8.

10. A positioning accuracy testing system, characterized in that: include: The electronic device as claimed in claim 9, and at least two devices to be positioned.

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