Data-driven UWB base station calibration error elimination method

Abstract

The invention discloses a data-driven UWB base station calibration error elimination method, which comprises the steps of S1, deducing label positioning with calibration errors, namely deducing a label positioning process with positioning errors based on an arrival time linear equation, and analyzing a base station calibration error correction method according to a solved label positioning expression; and S2, eliminating the calibration error of the base station in combination with experimental data, namely, collecting a real position and solving label position data by using a UWB positioning platform, a grating and switch equipment, and iteratively solving a coordinate system rotation matrix and a deviation vector based on the experimental data to realize calibration error correction. And correction of the calibration error of the base station is realized under relatively low calculation complexity.

Description

Technical Field

[0001] The invention relates to the field of wireless communication and network technology, and in particular to a data-driven UWB base station calibration error elimination method. Background Art

[0002] In recent years, with the development of location-based services (LBS) applications, high-precision positioning has become a research hotspot. At present, most location services still rely on the mature Global Positioning System (GPS), but this technology has the disadvantage of blocking positioning signals in scenes such as indoors, tunnels, and paths between buildings. Ultra-wideband (UWB) positioning technology is a better complementary positioning technology. Compared with other positioning technologies, UWB positioning has the advantages of high precision, high refresh rate, and resistance to multipath interference. With the standardization of UWB positioning frequency bands in my country, its market size is growing rapidly.

[0003] In the UWB positioning system, we usually use the location of the base station as a reference to solve the tag location, that is, the location of the base station needs to be calibrated in advance. Therefore, the base station layout has a certain impact on the UWB positioning accuracy, mainly reflected in: 1) The shape and coverage of the base station layout will affect the positioning accuracy; 2) The positioning error caused by the inaccurate calibration position of the base station. The first positioning error influencing factor can be obtained through theoretical deduction to obtain the optimal layout of the base station to achieve the optimal positioning performance. The second positioning error caused by the calibration position error in a fixed scene usually has a certain regularity, and a large amount of experimental data can be used to eliminate the calibration error.

[0004] It can be seen that the current calibration error elimination and positioning coordinate correction mainly need to solve the following problems: 1) the positioning error expression caused by the calibration error, and the mathematical relationship between the two; 2) the relationship between the actual position and the solved position in the experiment, and the selection of the conversion and positioning correction method between the two.

[0005] Therefore, a data-driven UWB base station calibration error elimination method is provided. Summary of the invention

[0006] The purpose of the present invention is to overcome the existing defects and provide a data-driven UWB base station calibration error elimination method, which realizes the correction of base station calibration error under lower calculation complexity.

[0007] The technical solution to achieve the above purpose is:

[0008] A data-driven UWB base station calibration error elimination method, comprising:

[0009] Step S1, deriving the tag positioning with calibration error, that is, based on the arrival time linear equation, deriving the tag positioning process with positioning error, and analyzing the base station calibration error correction method by solving the tag positioning expression;

[0010] Step S2, combining experimental data to eliminate base station calibration errors, that is, using the UWB positioning platform and grating and switch equipment to collect real position and solve tag position data, iteratively solve the coordinate system rotation matrix and deviation vector based on experimental data to achieve calibration error correction.

[0011] Preferably, the step S1 comprises:

[0012] Step S11, taking into account the ToA (wireless positioning) positioning equation form of the calibration error;

[0013] Step S12, solving the label coordinates based on the least squares method;

[0014] Step S13, analyzing and correcting the calibration error by using the coordinate system linear transformation method to obtain the optimal solution.

[0015] Preferably, in step S11, the real base station coordinates a are set i =(x i ,y i ,z i ) T , 1≤i≤N, construct the ToA linear equation Ax=b according to the ranging information, where x=(x,y,z) T , Δx represent the real coordinates and positioning error of the tag, respectively. 1 is the reference base station, the observation matrix A and the measurement vector b are expressed as:

[0016]

[0017] Taking into account the calibration error and the error in the measurement vector b, the observation matrix and the measurement vector are expressed as (A+ΔA), The corresponding ToA linear equation is:

[0018]

[0019] in, Indicates the ToA positioning result of the UWB tag.

[0020] Preferably, in step S12, based on the least squares method, the label coordinates are solved as follows:

[0021]

[0022] Preferably, in step S13, the label coordinates are sorted to obtain:

[0023]

[0024] Among them, the first It is related to the real position x of the label, and the second term is the positioning error term;

[0025] Linear change Perform calibration error correction, namely:

[0026]

[0027] when Get the optimal solution.

[0028] Preferably, step S2 comprises:

[0029] Step S21, arranging gratings on both sides of the moving direction of the trolley, multiple gratings are connected to the same switch, and the switch and the UWB main base station are connected to the same computer;

[0030] Step S22, collecting the real position of the vehicle passing through the grating and the position calculated by UWB;

[0031] Step S23, solving the coordinate system rotation matrix H and the deviation vector m according to the collected data;

[0032] Step S24, alternately iterate and solve until convergence or reaching the upper limit of the number of iterations, and correct the coordinates

[0033] Preferably, the step S22 includes:

[0034] Step S221, calibrate the positions of multiple gratings as the real positions, and the i-th group of data is represented by x i ;

[0035] Step S222, moving the car, when the car passes through the grating, the serial port data changes, and the serial port data timestamp at that moment is read;

[0036] Step S223, in the UWB positioning data, find the distance measurement data corresponding to the time stamp and solve the positioning coordinates, where the i-th group of data is represented as

[0037] Step S224, changing the position of the grating multiple times to obtain multiple sets of real positions and ToA tag positioning coordinates.

[0038] Preferably, in step S23, it is assumed that a total of N groups of experimental data are collected to construct the real position matrix X and the ToA positioning result matrix Right now:

[0039] X=(x 1 ,x2 ,…, x N );

[0040]

[0041] According to the optimization objective in step S13, an iterative method is used to solve the relevant parameters, and the initial values of the relevant parameter iteration are expressed as:

[0042]

[0043] Furthermore, the coordinate system rotation matrix and the deviation vector

[0044] Preferably, in step S24, the parameters are alternately iteratively updated, and the rotation matrix and deviation vector of the t-th iteration are expressed as:

[0045]

[0046] When the number of iterations t reaches the upper limit or the iteration change is less than a preset threshold tol, that is:

[0047] m(t) - m(t - 1)) T (m(t) - m(t - 1)) + tr{(H(t) - H(t - 1)) T (H(t) - H(t - 1))} < tol; stop the iteration and output the relevant calibration parameters

[0048] For the subsequent ToA positioning results Perform calibration using linear transformation to obtain the calibrated coordinates

[0049] The beneficial effects of the present invention are as follows: The UWB base station layout and calibration error correction method proposed by the present invention is applicable to the case of three-dimensional positioning of four or more base stations / two-dimensional positioning of three or more base stations. According to the ToA positioning linear equation, the tag positioning expression with base station calibration error is deduced; using devices such as gratings and switches, the real position and calculated position data collected from on-site experiments; based on the experimental data, the coordinate system is rotated and the positioning coordinates are calibrated, achieving an improvement in positioning accuracy. Description of the Drawings

[0050] Figure 1 is the flowchart of a data-driven UWB base station calibration error elimination method of the present invention;

[0051] Figure 2 is the specific flowchart of deducing the tag positioning with calibration error in the present invention;

[0052] Figure 3 It is a specific flow chart of eliminating base station calibration errors in combination with experimental data in the present invention;

[0053] Figure 4 It is a specific flow chart of collecting the real position of the vehicle passing through the grating and the UWB calculated position in the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0055] The present invention will be further described below in conjunction with the accompanying drawings.

[0056] like Figure 1 As shown, a data-driven UWB base station calibration error elimination method includes:

[0057] Step S1, deriving the tag positioning with calibration error, that is, based on the arrival time linear equation, deriving the tag positioning process with positioning error, and analyzing the base station calibration error correction method by solving the tag positioning expression.

[0058] like Figure 2 As shown, step S1 specifically includes:

[0059] Step S11, taking into account the ToA positioning equation form of the calibration error.

[0060] In the embodiment, the real base station coordinates a i =(x i ,y i ,z i ) T , 1≤i≤N, construct the ToA linear equation Ax=b according to the ranging information, where x=(x,y,z) T , Δx represent the real coordinates and positioning error of the tag, respectively. 1 is the reference base station, the observation matrix A and the measurement vector b are expressed as:

[0061]

[0062] Taking into account the calibration error and the error in the measurement vector b, the observation matrix and the measurement vector are expressed as (A+ΔA), The corresponding ToA linear equation is:

[0063]

[0064] in, Indicates the ToA positioning result of the UWB tag.

[0065] Step S12, solving the label coordinates based on the least squares method.

[0066] In the embodiment, based on the least squares method, the label coordinates are solved as follows:

[0067]

[0068] Step S13, analyzing and correcting the calibration error by using the coordinate system linear transformation method to obtain the optimal solution.

[0069] In the embodiment, the label coordinates are sorted to obtain:

[0070]

[0071] Among them, the first It is related to the real position x of the label, and the second term is the positioning error term;

[0072] Linear change Perform calibration error correction, namely:

[0073]

[0074] when Get the optimal solution.

[0075] Step S2, combining experimental data to eliminate base station calibration errors, that is, using the UWB positioning platform and grating and switch equipment to collect real position and solve tag position data, iteratively solve the coordinate system rotation matrix and deviation vector based on experimental data to achieve calibration error correction.

[0076] like Figure 3 As shown, step S2 specifically includes:

[0077] Step S21, gratings are arranged on both sides of the moving direction of the vehicle, and multiple gratings are connected to the same switch, and the switch and the UWB main base station are connected to the same computer.

[0078] Step S22, collecting the real position of the vehicle passing through the grating and the position calculated by UWB.

[0079] like Figure 4 As shown, step S22 specifically includes:

[0080] Step S221, calibrate the positions of multiple gratings as the real positions, and the i-th group of data is represented by x i .

[0081] Step S222, move the car. When the car passes through the grating, the serial port data changes, and the serial port data timestamp at that moment is read.

[0082] Step S223, in the UWB positioning data, find the distance measurement data corresponding to the time stamp and solve the positioning coordinates, where the i-th group of data is represented as

[0083] Step S224, changing the position of the grating multiple times to obtain multiple sets of real positions and ToA tag positioning coordinates.

[0084] Step S23, solving the coordinate system rotation matrix H and the deviation vector m based on the collected data.

[0085] In the embodiment, it is assumed that a total of N groups of experimental data are collected to construct the real position matrix X and the ToA positioning result matrix Right now:

[0086] X=(x 1 ,x 2 ,…,x N );

[0087]

[0088] According to the optimization target in step S13, the relevant parameters are solved by an iterative method, and the initial values ​​of the relevant parameter iterations are expressed as:

[0089]

[0090] Then we get the coordinate system rotation matrix and the deviation vector

[0091] Step S24, alternately iterate and solve until convergence or reaching the upper limit of the number of iterations, and correct the coordinates

[0092] In the embodiment, the parameters are updated by alternating iterations, and the rotation matrix and deviation vector of the t-th iteration are expressed as:

[0093]

[0094] When the number of iterations t reaches the upper limit or the iteration change is less than the preset threshold tol, that is:

[0095]

[0096] Stop iteration and output relevant correction parameters

[0097] For the subsequent ToA positioning results Use linear transformation to correct and get the corrected coordinates

[0098] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data-driven UWB base station calibration error elimination method, characterized in that: include: Step S1, deriving the tag positioning with calibration error, that is, based on the arrival time linear equation, deriving the tag positioning process with positioning error, and analyzing the base station calibration error correction method by solving the tag positioning expression; Step S2, combining experimental data to eliminate base station calibration errors, that is, using the UWB positioning platform and grating and switch equipment to collect real position and solve tag position data, iteratively solve the coordinate system rotation matrix and deviation vector based on experimental data to achieve calibration error correction.

2. A data-driven UWB base station calibration error elimination method according to claim 1, characterized in that: The step S1 comprises: Step S11, taking into account the ToA positioning equation form of the calibration error; Step S12, solving the label coordinates based on the least squares method; Step S13, analyzing and correcting the calibration error by using the coordinate system linear transformation method to obtain the optimal solution.

3. A data-driven UWB base station calibration error elimination method according to claim 2, characterized in that: In step S11, the real base station coordinates a are set i =(x i ,y i ,z i ) T , 1≤i≤N, construct the ToA linear equation Ax=b according to the ranging information, where x=(x,y,z) T , Δx represent the real coordinates and positioning error of the tag respectively. Taking base station a1 as the reference base station, the observation matrix A and the measurement vector b are respectively expressed as: Taking into account the calibration error and the error in the measurement vector b, the observation matrix and the measurement vector are expressed as The corresponding ToA linear equation is: in, Indicates the ToA positioning result of the UWB tag.

4. A data-driven UWB base station calibration error elimination method according to claim 2, characterized in that: In step S12, based on the least squares method, the label coordinates are solved as follows:

5. A data-driven UWB base station calibration error elimination method according to claim 2, characterized in that: In step S13, the label coordinates are sorted to obtain: Among them, the first It is related to the real position x of the label, and the second term is the positioning error term; Linear change Perform calibration error correction, namely: when Get the optimal solution.

6. A data-driven UWB base station calibration error elimination method according to claim 5, characterized in that: The step S2 comprises: Step S21, arranging gratings on both sides of the moving direction of the trolley, multiple gratings are connected to the same switch, and the switch and the UWB main base station are connected to the same computer; Step S22, collecting the real position of the vehicle passing through the grating and the position calculated by UWB; Step S23, solving the coordinate system rotation matrix H and the deviation vector m according to the collected data; Step S24, alternately iterate and solve until convergence or reaching the upper limit of the number of iterations, and correct the coordinates 7. A data-driven UWB base station calibration error elimination method according to claim 6, characterized in that: The step S22 comprises: Step S221, calibrate the positions of multiple gratings as the real positions, and the i-th group of data is represented by x i ; Step S222, moving the car, when the car passes through the grating, the serial port data changes, and the serial port data timestamp at that moment is read; Step S223, in the UWB positioning data, find the distance measurement data corresponding to the time stamp and solve the positioning coordinates, where the i-th group of data is represented as Step S224, changing the position of the grating multiple times to obtain multiple sets of real positions and ToA tag positioning coordinates.

8. A data-driven UWB base station calibration error elimination method according to claim 6, characterized in that: In step S23, it is assumed that a total of N groups of experimental data are collected to construct the real position matrix X and the ToA positioning result matrix Right now: X=(x1,x2,…,x N ); According to the optimization target in step S13, an iterative method is used to solve the relevant parameters, and the initial value of the relevant parameter iteration is expressed as: Then we get the coordinate system rotation matrix and the deviation vector 9. A data-driven UWB base station calibration error elimination method according to claim 6, characterized in that: In step S24, the parameters are updated by alternating iterations, and the rotation matrix and deviation vector of the t-th iteration are expressed as: When the number of iterations t reaches the upper limit or the iteration change is less than the preset threshold tol, that is: Stop iteration and output relevant correction parameters For the subsequent ToA positioning results Use linear transformation to correct and get the corrected coordinates

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