Positioning data processing method, apparatus and device

Abstract

The present application relates to the field of positioning, and provides a positioning data processing method, device and equipment, the method comprising: synchronously collecting radar target data and GPS trajectory data, selecting a target data sequence from the radar target data that coincides in time with the GPS trajectory data; selecting a time-continuous target coordinate point sequence from target coordinate points converted from the GPS trajectory data; determining a first coordinate sequence corresponding to the target coordinate points from the target data sequence, determining a coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence; determining a coordinate conversion parameter based on the coordinate pair sequence, and converting radar coordinates to be processed into GPS coordinates through the coordinate conversion parameter. The present application realizes automatic conversion between radar coordinates and GPS coordinates, avoids tedious manual operation, and improves coordinate conversion efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of positioning technology, and in particular to a positioning data processing method, apparatus, and device. Background Technology

[0002] During the construction of holographic intersections, radar-based cameras are typically installed at intersections to detect and track moving targets, thereby obtaining real-time vehicle trajectories. Since the radar-based target trajectory is based on radar coordinates, it needs to be converted to GPS (Global Positioning System) coordinates. This conversion process relies on solving for the radar origin coordinates and the north angle. However, existing technical solutions require high-precision maps of the intersection, precise positioning and calibration using surveying equipment on-site, or repeated adjustments through multiple driving tests at the intersection when solving for these coordinates. This involves numerous manual steps, high complexity, and is prone to errors; furthermore, the operational methods cannot be completely replicated at every intersection, resulting in low efficiency. Summary of the Invention

[0003] This invention provides a positioning data processing method, apparatus, and device to solve the technical problems of high complexity and low efficiency in the existing process of converting radar coordinates to GPS coordinates.

[0004] This invention provides a positioning data processing method, comprising:

[0005] Simultaneously collect radar target data and GPS trajectory data, and select target data sequences that coincide with the time of the GPS trajectory data from the radar target data;

[0006] Select a time-continuous sequence of target coordinate points from the target coordinate points, wherein the target coordinate points are obtained by converting the GPS trajectory data;

[0007] Determine the first coordinate sequence corresponding to the target coordinate point from the target data sequence, and determine the coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence;

[0008] Based on the coordinate pair sequence, coordinate transformation parameters are determined, and the radar coordinates to be processed are converted into GPS coordinates using the coordinate transformation parameters.

[0009] According to a positioning data processing method provided by the present invention, the GPS trajectory data includes GPS time and latitude and longitude, and the radar target data includes target identifier, radar time, and radar coordinates; the step of selecting a target data sequence from the radar target data that coincides with the time of the GPS trajectory data includes:

[0010] Obtain GPS trajectory data sequence of targets within a latitude and longitude region within a unit time period, and obtain radar target data sequence of targets within a radar detection region within a unit time period;

[0011] Filter out the target dataset that overlaps with the time of the GPS trajectory data sequence from the radar target data sequence;

[0012] Based on the target identifier, the time-continuous points in the target data are combined to form a target data sequence.

[0013] According to a positioning data processing method provided by the present invention, the positioning data processing method further includes:

[0014] The GPS coordinates in the GPS trajectory data sequence are converted into target coordinate points to obtain a target coordinate point sequence composed of the target coordinate points.

[0015] According to a positioning data processing method provided by the present invention, determining the first coordinate sequence corresponding to the target coordinate point from the target data sequence includes:

[0016] The target data sequences are traversed, and the target data sequences and target coordinate point sequences are matched using a trajectory matching algorithm;

[0017] Based on the matching results, the first coordinate sequence corresponding to the target coordinate point is determined from the target data sequence.

[0018] According to a positioning data processing method provided by the present invention, the trajectory matching algorithm includes a distance-based nearest neighbor matching algorithm and a time series-based matching algorithm;

[0019] The process of matching the target data sequence and the target coordinate point sequence includes:

[0020] The target data sequence and the target coordinate point sequence are preprocessed, including data cleaning and filtering.

[0021] According to a positioning data processing method provided by the present invention, determining the coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence includes:

[0022] Construct a pair between the target coordinate point and the first coordinate sequence;

[0023] Based on the pair, the target coordinate point is associated with the first coordinate sequence to obtain the coordinate pair sequence.

[0024] According to a positioning data processing method provided by the present invention, determining the coordinate transformation parameters based on the coordinate pair sequence includes:

[0025] The radar coordinates and GPS coordinates are fitted by a constructed transformation model, which includes a linear regression model and a multinomial fitting model.

[0026] Based on the fitting results and the coordinate pair sequence, coordinate transformation parameters are determined, including rotation angles and translation vectors.

[0027] According to a positioning data processing method provided by the present invention, the step of converting the radar coordinates to be processed into GPS coordinates through the coordinate transformation parameters includes:

[0028] Based on the transformation model and the coordinate transformation parameters, the radar coordinates to be processed are converted into GPS coordinates.

[0029] The present invention also provides a positioning data processing device, comprising:

[0030] The target data sequence filtering module is used to simultaneously collect radar target data and GPS trajectory data, and select target data sequences that coincide with the time of the GPS trajectory data from the radar target data;

[0031] The target coordinate point sequence filtering module is used to select a time-continuous sequence of target coordinate points from the target coordinate points, wherein the target coordinate points are obtained by converting the GPS trajectory data;

[0032] A coordinate pair sequence determination module is used to determine a first coordinate sequence corresponding to the target coordinate point from the target data sequence, and to determine a coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence;

[0033] The coordinate transformation module is used to determine coordinate transformation parameters based on the coordinate pair sequence, and to convert the radar coordinates to be processed into GPS coordinates through the coordinate transformation parameters.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the positioning data processing method as described above.

[0035] The positioning data processing method, apparatus, and equipment provided by this invention fully consider the problems of low efficiency and large error in the solution of radar position and north angle parameters during the coordinate transformation of radar target data at intersections. By simultaneously collecting radar target data and vehicle GPS data during a single vehicle trip, the radar trajectory and GPS trajectory are paired according to the trajectory matching algorithm to determine the similarity between the two trajectories. Then, the coordinate transformation parameters are solved using the indirect adjustment method, and the transformation between radar coordinates and GPS coordinates is realized through the solved coordinate transformation parameters. This achieves automatic transformation between radar coordinates and GPS coordinates, avoids tedious manual operation, and improves the efficiency and accuracy of coordinate transformation. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is one of the flowcharts illustrating the positioning data processing method provided by the present invention;

[0038] Figure 2 This is a basic flowchart of the positioning data processing method provided by the present invention;

[0039] Figure 3 This is a timing diagram of the positioning data processing method provided by the present invention;

[0040] Figure 4 This is the second flowchart of the positioning data processing method provided by the present invention;

[0041] Figure 5 This is a schematic diagram of the positioning data processing device provided by the present invention;

[0042] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0044] Please refer to Figure 1The present invention provides a positioning data processing method, comprising:

[0045] Step 100: Simultaneously collect radar target data and GPS trajectory data, and select target data sequences from the radar target data that coincide with the time of the GPS trajectory data;

[0046] Specifically, the basic flow of the positioning data processing method provided by the present invention is as follows:

[0047] First, install GPS positioning devices in the vehicle to collect real-time GPS trajectory data (including GPS time and latitude / longitude, etc.), such as Figure 2 S1 and Figure 3 "Collect GPS trajectory data" in the text;

[0048] Second, the radar-video (a fusion of radar and video) data acquisition service is activated simultaneously with the GPS positioning device, and the radar-video data of the vehicle (i.e., the radar-video target data in this embodiment) is collected in real time. The radar-video target data includes radar-video time, target identifier, and radar-video coordinates, such as... Figure 2 S2 and Figure 3 "Collecting radar target data" in the middle;

[0049] Third, the vehicle travels through each radar-detection zone at the intersection, with its trajectory covering every lane within that zone. This yields a second-by-second GPS trajectory data sequence G and a radar-detection target data sequence O during the driving time period. Figure 2 S3 in the middle;

[0050] Fourth, the GPS trajectory is divided according to the latitude and longitude regions of the intersections. Each latitude and longitude region corresponds to a subset {G1, G2, ..., Gn}, which is the GPS trajectory data in this embodiment, such as... Figure 2 S4 in the middle;

[0051] Fifth, select the target dataset {O1, O2, ..., On} from the radar target data that overlaps with the GPS trajectory data in time, i.e., the target data sequence in this embodiment, such as... Figure 2 S6 in the middle.

[0052] Step 200: Select a time-continuous sequence of target coordinate points from the target coordinate points, wherein the target coordinate points are obtained by converting the GPS trajectory data;

[0053] The positioning data processing method provided in this application embodiment may further include:

[0054] Step 10: Convert the GPS coordinates in the GPS trajectory data sequence into target coordinate points to obtain a target coordinate point sequence composed of the target coordinate points.

[0055] Specifically, the basic flow of the positioning data processing method provided by the present invention further includes:

[0056] Sixth, convert each GPS track coordinate into Universal Transverse Mercartor Grid System (UTM) coordinates to obtain multiple UTM point arrays {U1, U2, ..., Un}, such as... Figure 2 As shown in S5, GPS coordinates can be converted to UTM coordinates using a specific code program.

[0057] Step 300: Determine the first coordinate sequence corresponding to the target coordinate point from the target data sequence, and determine the coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence;

[0058] The positioning data processing method provided in this application embodiment may further include:

[0059] Step 310: Traverse each of the target data sequences and match the target data sequences and the target coordinate point sequences using a trajectory matching algorithm; the trajectory matching algorithm includes a distance-based nearest neighbor matching algorithm and a time series-based matching algorithm;

[0060] The positioning data processing method provided in this application embodiment may further include:

[0061] Step 311: Perform data preprocessing on the target data sequence and the target coordinate point sequence. The data preprocessing includes data cleaning and filtering.

[0062] Step 320: Determine the first coordinate sequence corresponding to the target coordinate point from the target data sequence based on the matching result;

[0063] Step 330: Construct a pair between the target coordinate point and the first coordinate sequence;

[0064] Step 340: Based on the pair, the target coordinate point is associated with the first coordinate sequence to obtain the coordinate pair sequence.

[0065] Based on the dual pair, the target coordinate point sequence {A1, A2, A3, ..., An} is associated with the first coordinate sequence {B1, B2, B3, ..., Bn} one by one in the order of one trajectory point per second, to obtain the coordinate dual pair sequence {(A1, B1), (A2, B2), (A3, B3), ..., (An, Bn)}.

[0066] Specifically, the basic flow of the positioning data processing method provided by the present invention further includes:

[0067] Seventh, construct a sequence {U1, U2, ..., Un} from the time-continuous points in the UTM according to their time order, such as... Figure 2 S7 in the middle;

[0068] Eighth, group the target data sequence from step five above according to object identifiers, and arrange the time-continuous points into the target data sequence {O1, O2, ..., On} in chronological order, such as... Figure 2 S8 in the middle;

[0069] Ninth, traverse each UTM sequence (i.e., the target data sequence in this embodiment), and find the target sequence (i.e., the first coordinate sequence in this embodiment) corresponding to the UTM data (i.e., the target coordinate points in this embodiment) from the target data sequence according to the LCSS trajectory matching algorithm. Based on the matching results, establish the correspondence between the radar trajectory (first coordinate point sequence) and the GPS trajectory (target coordinate point sequence). Construct pairs of target coordinate points and the first coordinate sequence, for example, <[U1, U2, ..., Un], [O1, O2, ..., On]>, such as... Figure 2 S9 in the algorithm; among them, LCSS (Longest Common Subsequence) is a well-known trajectory similarity algorithm based on sequence matching. Its core idea is to find the longest common subsequence between two trajectories and then calculate the similarity between the two trajectories. The LCSS algorithm has a time complexity of O(n^2) and is suitable for relatively small trajectory datasets.

[0070] Tenth, associate the UTM data sequence with the first coordinate sequence to obtain the coordinate pair sequence.<U1,O1> ,<U2,O2> ,...,<Un,On> ],like Figure 2 S10 in the middle.

[0071] The trajectory matching algorithm in this embodiment can be a distance-based nearest neighbor matching algorithm or a time-series-based matching algorithm. Other suitable algorithms are also possible, but this embodiment does not limit the specific algorithms used.

[0072] Distance-based nearest neighbor matching algorithms are divided into single-point nearest neighbor matching algorithms and global nearest neighbor matching algorithms. This embodiment does not specify which nearest neighbor matching algorithm to use.

[0073] The single-point nearest neighbor matching algorithm is explained as follows: For each point x in the target data sequence... i Determine the distance x in the target coordinate point sequence. i The nearest point y i That is, min{||x i -y i||}. Here, ||·|| represents the Euclidean distance. The global nearest neighbor matching algorithm is explained as follows: For a target data sequence X and a target coordinate point sequence Y, calculate the global distance D(X, Y) between the two sequences. Common methods for calculating the global distance between two sequences include Euclidean distance, Manhattan distance, and Mahalanobis distance.

[0074] Time-series-based matching algorithms can be categorized into Dynamic Time Warping (DTW), Shape Averaging (SAM), and Local Feature Analysis (LFA). In this embodiment, the time-series-based matching algorithm can be any one of DTW, SAM, and LFA, or any combination thereof, or other algorithms that meet the requirements and achieve the same function. This embodiment does not impose any limitations on these algorithms.

[0075] The Dynamic Time Warping (DTW) algorithm is explained as follows: For two time series X and Y, the DTW distance between them is defined as min{(x... i -y j ) 2}, where i and j are the indices of X and Y, respectively, and satisfy the following two conditions: 0≤i≤m-1, 0≤j≤n-1; m and n are the lengths of X and Y, respectively. The shape averaging method is explained as follows: For two time series X and Y, the SAM distance between them is defined as (1 / m)*∑|x i -y i | where i is the index of X and Y, and m is the length of the sequence; the local feature analysis method is explained as follows: For two time series X and Y, calculate their local feature vectors FX and FY, and then define the LFA distance between them as (1 / m)*∑min{||FX||FY ... i -FY j ||}2, where i and j are the indices of X and Y respectively, and m is the length of the sequence.

[0076] Step 400: Determine coordinate transformation parameters based on the coordinate pair sequence, and convert the radar coordinates to be processed into GPS coordinates using the coordinate transformation parameters.

[0077] The positioning data processing method provided in this application embodiment may further include:

[0078] Step 410: Fit the radar coordinates and the GPS coordinates using the constructed transformation model, which includes a linear regression model and a multinomial fitting model.

[0079] Step 420: Based on the fitting results and the coordinate pair sequence, determine the coordinate transformation parameters, which include rotation angles and translation vectors.

[0080] Step 430: Based on the transformation model and the coordinate transformation parameters, convert the radar coordinates to be processed into GPS coordinates.

[0081] Specifically, the basic flow of the positioning data processing method provided by the present invention further includes:

[0082] Eleventh, using the four-parameter coordinate transformation formula, construct a system of transformation equations from Raikkonen coordinates to UTM coordinates, such as... Figure 2 S21 in;

[0083] Twelfth, substitute the UTM data sequence [U1, U2, U3, ..., Un] and the target data sequence [O1, O2, O3, ..., Un] into the transformation equations, and use the indirect adjustment method to obtain the coordinate transformation parameters, such as... Figure 2 S31 and Figure 3 The "Solve for Transformation Parameters" section refers to the UTM coordinate system, a Cartesian coordinate system whose format is: longitude zone / latitude zone / east / north, where east represents the projected distance from the central meridian of the longitude zone, and north represents the projected distance from the equator (unit: meters). GPS coordinates and UTM coordinates can be converted to each other. The radar coordinate system is a Cartesian coordinate system, with the Y-axis pointing towards the radar normal and the X-axis perpendicular to the Y-axis, conforming to the right-hand coordinate system rule. Targets detected by the radar have unique (x, y) coordinates, representing the distance of the target from the radar's X and Y axes (unit: meters).

[0084] Thirteenth, given the radar coordinates of any point (i.e., the radar coordinates to be processed in this embodiment), the GPS coordinates are obtained by converting them according to the conversion formula and coordinate transformation parameters, such as... Figure 2 S41 and S51 in, and Figure 3 "Data coordinate transformation" in the context of data coordinate transformation.

[0085] The process from step 11 to step 13 above can be summarized as follows: Based on the established correspondence between the radar-view trajectory and the GPS trajectory, a suitable mathematical model (such as a linear regression model and a polynomial fitting model) is used to fit the radar-view trajectory and the GPS trajectory; then, based on the fitting results, coordinate transformation parameters (such as rotation angles and translation vectors) are obtained; and using the obtained coordinate transformation parameters, the radar-view coordinates are converted into GPS coordinates.

[0086] The linear regression model and the polynomial fitting model in this embodiment are both statistical models used to predict and analyze the relationship between data (Rayet coordinates and GPS coordinates).

[0087] The linear regression model is explained below:

[0088] Linear regression is a simple yet powerful predictive model used to predict the relationship between a response variable (dependent variable) and one or more predictor variables (independent variables). In this model, the relationship between the predictor and response variables is assumed to be linear. The general form of a linear regression model is: Y = β0 + β1X1 + β2X2 + ... + β n Xn+ε, where Y is the response variable, X1, X2, ..., Xn are the predictor variables, and β0, β1, ..., βn are the predictor variables. n These are the model parameters to be estimated, and ε is the error term. When fitting the radar line coordinates and GPS coordinates, the radar line coordinates can be used as the response variable, and the GPS coordinates as the predictor variable. Then, the linear regression model described above can be used to estimate the relationship between the radar line coordinates and the GPS coordinates.

[0089] The polynomial fitting model is explained as follows:

[0090] A polynomial fitting model is a nonlinear model that describes the relationship between the response variable and the predictor variable by expressing the predictor variable as a polynomial. The general form of a polynomial fitting model is: Y = β0 + β1X1 + β2X1 2 +β3X1 3 +...+a n Xn, where Y is the response variable, X1, X2, ..., Xn are the predictor variables, β0, β1, ..., a n These are the model parameters to be estimated. When fitting radar and GPS coordinates, a multinomial fitting model can be used to describe the relationship between them. Specifically, GPS coordinates can be used as the predictor variable, radar coordinates as the response variable, and then a multinomial fitting model can be used to estimate the relationship between the radar and GPS coordinates.

[0091] This embodiment fully considers the problems of low efficiency and large errors in the solution and calibration of parameters such as the installation position and north angle of the radar sight during the coordinate transformation of intersection radar sight data. It simultaneously collects radar sight target data and vehicle GPS data during a single vehicle trip, then pairs the radar sight target trajectory with the GPS trajectory using the LCSS trajectory matching algorithm, and finally uses the indirect adjustment method to solve for the coordinate transformation parameters, thereby automating the solution of coordinate transformation parameters, improving the efficiency of coordinate transformation, and reducing coordinate transformation errors. The indirect adjustment method involves selecting independent quantities that have no conditional relationship with each other as unknowns when determining the most probable values ​​of multiple unknowns (the most likely true values ​​of the observed object that can be obtained from a series of observations). This forms a functional relationship expressing the measurement using the unknowns, lists the error equations, and uses the least squares method to obtain the most probable values ​​of the unknowns. Indirect adjustment is the most commonly used method in adjustment calculations; its mathematical model is not complex, making it easy to evaluate the accuracy of the adjusted values ​​and their functions.

[0092] This application can also be applied to various fields, such as intelligent transportation, drone navigation, and robot positioning. It can effectively convert and fuse target information detected by radar with GPS navigation information, improving the accuracy of target detection and navigation.

[0093] Please refer to Figure 4 In one embodiment, the GPS trajectory data includes latitude and longitude, and the radar target data includes target identifiers. The positioning data processing method provided in this application embodiment may further include:

[0094] Step 110: Obtain the GPS trajectory data sequence of the target within the latitude and longitude area within a unit time, and obtain the radar target data sequence of the target within the radar detection area within a unit time.

[0095] Step 120: Select target datasets from the radar target data sequence that overlap with the time of the GPS trajectory data sequence;

[0096] Step 130: Based on the target identifier, form a target data sequence from the points that are sequentially continuous in time in the target data set.

[0097] Specifically, as shown in the first to fifth steps of the basic flow of the positioning data processing method provided by the present invention, wherein:

[0098] 1. Install a GPS positioning device on the vehicle to collect GPS trajectory data in real time; 2. Enable the radar-view data collection service to collect radar-view data of the vehicle in real time, including radar-view target data such as time, object identifier, and target coordinates; 3. Drive the vehicle through each radar-view detection area of ​​the intersection, with the driving trajectory covering each lane within the radar-view detection area, to obtain the GPS trajectory data sequence G and the radar-view target data sequence O per second during the driving time period; 4. Divide the GPS trajectory according to the latitude and longitude regions of the intersection (i.e., the latitude and longitude regions in this embodiment), with each latitude and longitude region corresponding to a subset {G1, G2, ..., Gn}, which is the GPS trajectory data in this embodiment; 5. Select the target dataset {O1, O2, ..., On} from the radar-view target data sequence that overlaps with the time of the GPS trajectory data, which is the target data sequence in this embodiment.

[0099] This embodiment solves for each parameter through a program, realizing the programmatic conversion of target data at intersections and improving conversion efficiency.

[0100] This application has the following beneficial effects:

[0101] 1. When solving for parameters, GPS trajectory data and radar target data are collected at once, eliminating the need for repeated debugging at the collection site.

[0102] 2. Reduce manual operations and lower the error rate caused by human error.

[0103] 3. By solving for each parameter through the program, the target data of the intersection is automatically converted, which improves the conversion efficiency.

[0104] 4. No special processing is required for each intersection, making it widely applicable and suitable for coordinate transformation of target data at various intersections.

[0105] The positioning data processing apparatus provided by the present invention is described below. The positioning data processing apparatus described below and the positioning data processing method described above can be referred to in correspondence.

[0106] Please refer to Figure 5 The present invention also provides a positioning data processing device, comprising:

[0107] The target data sequence filtering module 501 is used to simultaneously collect radar target data and GPS trajectory data, and select target data sequences that coincide with the time of the GPS trajectory data from the radar target data;

[0108] The target coordinate point sequence filtering module 502 is used to select a time-continuous sequence of target coordinate points from the target coordinate points, wherein the target coordinate points are obtained by converting the GPS trajectory data;

[0109] The coordinate pair sequence determination module 503 is used to determine the first coordinate sequence corresponding to the target coordinate point from the target data sequence, and to determine the coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence;

[0110] The coordinate transformation module 504 is used to determine coordinate transformation parameters based on the coordinate pair sequence, and to convert the radar coordinates to be processed into GPS coordinates through the coordinate transformation parameters.

[0111] Optionally, the GPS trajectory data includes GPS time and latitude / longitude, and the radar target data includes target identifier, radar time, and radar coordinates; the target data sequence filtering module includes:

[0112] The data sequence acquisition unit is used to acquire the GPS trajectory data sequence of targets within the latitude and longitude area within a unit time, and to acquire the radar target data sequence of targets within the radar detection area within a unit time.

[0113] The target dataset filtering unit is used to filter out target datasets that overlap with the time of the GPS trajectory data sequence from the radar target data sequence;

[0114] The target data sequence determination unit is used to form a target data sequence from the time-continuous points in the target data based on the target identifier.

[0115] Optionally, the positioning data processing device further includes:

[0116] The target coordinate point sequence determination module is used to convert the GPS coordinates in the GPS trajectory data sequence into target coordinate points, thereby obtaining a target coordinate point sequence composed of each target coordinate point.

[0117] Optionally, the coordinate pair sequence determination module includes:

[0118] A sequence matching unit is used to traverse each of the target data sequences and match the target data sequences with the target coordinate point sequences using a trajectory matching algorithm;

[0119] The first coordinate sequence determination unit is used to determine the first coordinate sequence corresponding to the target coordinate point from the target data sequence based on the matching result.

[0120] Optionally, the trajectory matching algorithm includes a distance-based nearest neighbor matching algorithm and a time-series-based matching algorithm; the sequence matching unit includes:

[0121] A data preprocessing unit is used to perform data preprocessing on the target data sequence and the target coordinate point sequence, wherein the data preprocessing includes data cleaning and filtering.

[0122] Optionally, the coordinate pair sequence determination module further includes:

[0123] A pair construction unit is used to construct pairs between the target coordinate point and the first coordinate sequence;

[0124] The coordinate pair sequence determination unit is used to associate the target coordinate point with the first coordinate sequence based on the pair to obtain the coordinate pair sequence.

[0125] Optionally, the coordinate transformation module includes:

[0126] A coordinate fitting unit is used to fit the radar coordinates and the GPS coordinates using a constructed transformation model, wherein the transformation model includes a linear regression model and a multinomial fitting model.

[0127] The coordinate transformation parameter determination unit is used to determine the coordinate transformation parameters based on the fitting results and the coordinate pair sequence, wherein the coordinate transformation parameters include rotation angles and translation vectors.

[0128] Optionally, the coordinate transformation module further includes:

[0129] The coordinate transformation unit is used to convert the radar coordinates to be processed into GPS coordinates based on the transformation model and the coordinate transformation parameters.

[0130] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions stored in the memory 630 to execute a positioning data processing method.

[0131] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0132] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the positioning data processing methods provided by the methods described above.

[0133] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A positioning data processing method, characterized in that, include: Simultaneously collect radar target data and GPS trajectory data, and select target data sequences that coincide with the time of the GPS trajectory data from the radar target data; Select a time-continuous sequence of target coordinate points from the target coordinate points; the target coordinate points are converted from the GPS trajectory data into transmerkato grid system coordinates to obtain multiple UTM point arrays; The target data sequences are traversed, and the target data sequences and target coordinate point sequences are matched using a trajectory matching algorithm; Based on the matching results, a first coordinate sequence corresponding to the target coordinate point is determined from the target data sequence, and a coordinate pair sequence is determined based on the target coordinate point sequence and the first coordinate sequence; The radar coordinates and GPS coordinates are fitted by a constructed transformation model, which includes a linear regression model and a multinomial fitting model. Based on the fitting results and the coordinate pair sequence, coordinate transformation parameters are determined, including rotation angles and translation vectors; the radar coordinates to be processed are converted into GPS coordinates using the coordinate transformation parameters.

2. The positioning data processing method according to claim 1, characterized in that, The GPS trajectory data includes GPS time and latitude / longitude, and the radar target data includes target identifier, radar time, and radar coordinates; selecting the target data sequence that coincides with the time of the GPS trajectory data from the radar target data includes: Obtain GPS trajectory data sequence of targets within a latitude and longitude region within a unit time period, and obtain radar target data sequence of targets within a radar detection region within a unit time period; Filter out the target dataset that overlaps with the time of the GPS trajectory data sequence from the radar target data sequence; Based on the target identifier, the time-continuous points in the target data are combined to form a target data sequence.

3. The positioning data processing method according to claim 2, characterized in that, The location data processing method further includes: The GPS coordinates in the GPS trajectory data sequence are converted into target coordinate points to obtain a target coordinate point sequence composed of the target coordinate points.

4. The positioning data processing method according to claim 1, characterized in that, The trajectory matching algorithm includes a distance-based nearest neighbor matching algorithm and a time series-based matching algorithm; The process of matching the target data sequence and the target coordinate point sequence includes: The target data sequence and the target coordinate point sequence are preprocessed, including data cleaning and filtering.

5. The positioning data processing method according to claim 1, characterized in that, The step of determining the coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence includes: Construct a pair between the target coordinate point and the first coordinate sequence; Based on the pair, the target coordinate point is associated with the first coordinate sequence to obtain the coordinate pair sequence.

6. The positioning data processing method according to claim 1, characterized in that, The process of converting the radar coordinates to be processed into GPS coordinates using the coordinate transformation parameters includes: Based on the transformation model and the coordinate transformation parameters, the radar coordinates to be processed are converted into GPS coordinates.

7. A positioning data processing device, characterized in that, include: The target data sequence filtering module is used to simultaneously collect radar target data and GPS trajectory data, and select target data sequences that coincide with the time of the GPS trajectory data from the radar target data; The target coordinate point sequence filtering module is used to select a time-continuous sequence of target coordinate points from the target coordinate points. The target coordinate points are converted from the GPS trajectory data into trans-Mercator grid system coordinates to obtain multiple UTM point arrays. The coordinate pair sequence determination module is used to traverse each of the target data sequences, match the target data sequences and the target coordinate point sequences using a trajectory matching algorithm, determine the first coordinate sequence corresponding to the target coordinate point from the target data sequences based on the matching results, and determine the coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence. The coordinate transformation module is used to fit radar coordinates and GPS coordinates using a constructed transformation model, which includes a linear regression model and a multinomial fitting model; based on the fitting results and the coordinate pair sequence, coordinate transformation parameters are determined, which include rotation angles and translation vectors; and the radar coordinates to be processed are converted into GPS coordinates using the coordinate transformation parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the positioning data processing method as described in any one of claims 1 to 6.

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