CN113850908A - Optimization method of ground flash back positioning data considering path extension factor - Google Patents
Optimization method of ground flash back positioning data considering path extension factor Download PDFInfo
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Abstract
The application relates to an optimization method of ground flashback location data considering path extension factors, which comprises the steps of firstly determining the level of influence of different characteristic parameters of an observation substation on the ground flashback location precision; then, gridding the positioning precision distribution of the observation substation according to the determined different grades; determining the positioning errors of different grids according to the propagation time delay of the ground flashback electromagnetic field caused by the complex terrain; then, according to different requirements on ground flash back impact positioning errors, removing lightning data in grids with large positioning errors; and finally, determining the difference before and after the optimization of the lightning positioning data according to preset comparison data, and evaluating the quality control effect of the lightning positioning data. The method provided by the application is characterized in that the additional time deviation brought by a terrain propagation path is added on the inherent time deviation of the observation substation hardware, and the Monte Carlo algorithm is utilized to realize the real-time optimization and processing of the positioning deviation problem of the observation substation.
Description
Technical Field
The application relates to the technical field of lightning detection and early warning service in a strong convection disaster weather process, in particular to an optimization method of ground flashback positioning data considering path extension factors.
Background
A positioning algorithm of a multi-station time difference (TOA) lightning positioning technology based on a GPS technology is a commonly used positioning algorithm at present, and the positioning accuracy of the TOA is greatly improved by high-precision GPS time along with the development of the GPS technology in recent years. For long baseline time difference lightning location systems, the main source of error is uncertainty in the time measurement. In the lightning electromagnetic wave propagation process, both complex terrains and uneven conductivity have certain influence on the time of the pulse reaching each observation substation, and for the lightning electromagnetic waves propagated in long distance, the reaching time is also influenced by the curvature of the earth and the refractive index of the atmosphere; in addition, the better the time synchronism among the observation substations is, the more the observation substations participating in positioning are, the higher the positioning precision is; finally, the electromagnetic environment of the observation substation is coupled with the electromagnetic wave reaching the observation substation, so that the lightning electromagnetic wave recorded by the observation substation is distorted, and the identification and positioning of the pulse waveform are influenced.
In the TOA positioning technology, the distance between observation sub-stations is fixed, a hyperbola can be constructed by using the time difference of lightning signals reaching two observation sub-stations, and a plurality of hyperbolas constructed by a plurality of observation sub-stations are intersected at a certain point, namely the position of a lightning radiation origin. The three-station time difference can be calculated by a hyperboloid method to obtain two-dimensional longitude and latitude coordinates of the radiation source point, the four-station time difference can be calculated by the hyperboloid method to obtain three-dimensional space coordinates of the radiation source point, for five-station time difference data, the three-dimensional space coordinates and the occurrence time of the radiation source point are calculated by solving a nonlinear equation system, and more synchronous time information is used for optimizing the positioning result. The accuracy of TOA positioning is directly related to the error of time measurement, and the influence of terrain or environmental disturbance may cause the error of TOA positioning in meter scale to kilometer scale.
Errors in time measurement are mainly due to several sources: (1) extension of the terrain-induced electromagnetic signal arrival time; (2) GPS time service precision; (3) time of arrival/time difference calculated by different methods. Wherein the geodetic error caused by the terrain is related to the degree of relief of the terrain, and for long-wave signals propagating along the surface, the statistical result indicates that the geodetic error caused by the terrain is about 1 mus every 100 km. Meanwhile, due to dispersion and interference effects in the process of electromagnetic wave propagation, the waveform is distorted, and rising edge parameters and peak time of the waveform are changed to different degrees. The GPS time service precision is influenced by the installed time service system, and the time measurement error may be different from 50ns to 200 ns. It can be seen that the time error caused by the terrain has a larger influence on the positioning result, which also results in a larger deviation of the TOA positioning result in a mountain area with rugged terrain. For example, Schulz and Diendorfer use a real terrain propagation path to replace a straight line path in an IMPACT algorithm, the distance between a lightning positioning point after correction and a radio tower serving as a reference is reduced, and the time deviation is reduced to a certain extent. Li et al further compared the two-dimensional TOA positioning algorithm correcting effects of the terrain-envelope method and the light-terrain-fitmethodd, and also proved that the influence of the roughness and the high and low fluctuation characteristics of the terrain needs to be considered in the lightning positioning of mountainous terrain.
The mountain terrain has a large influence on the positioning result of the TOA algorithm based on ideal assumed conditions, namely that signals are assumed to propagate to an observation substation along a straight line without loss of light speed, and adverse influence is possibly brought to lightning protection in mountainous areas, so that the influence of rugged terrains such as mountains on lightning positioning needs to be deeply researched, and the lightning positioning algorithm is improved by combining real terrain data.
The existing foundation lightning detection network positions the position of a radiation source point based on the related theory of electromagnetic field propagation and inverts physical parameters such as current amplitude and the like. In the process of propagation of the lightning electromagnetic wave, the lightning electromagnetic wave is influenced by various factors, and the waveform parameters, such as rising edge time, change rate, waveform peak value and the like, of the electromagnetic signal when reaching a remote observation substation are deviated from theoretical values to different degrees, so that the positioning precision of a detection network and the accuracy of an inversion result are influenced. In a relatively short distance, an electromagnetic radiation field generated by the ground strike-back channel is mainly transmitted to the observation substation in the form of ground waves, and influence of rugged terrains such as mountains on propagation of the strike-back electromagnetic field is often not negligible. In the positioning algorithm, it is generally assumed that an electromagnetic signal generated by lightning propagates to an observation substation along a straight line at an optical speed, the ground surface is smooth, the conductivity is infinite, and a lightning electromagnetic field is influenced by effects such as dispersion and diffraction in the propagation process of a real environment, so that the accuracy of lightning positioning is reduced, and a lightning parameter inversion result has a large error. Therefore, it is necessary to make an intensive study and discussion on the problem of the propagation of the lightning electromagnetic wave in the complex terrain, and correct the waveform change caused by the undulating terrain such as mountain, so as to improve the accuracy of the positioning and inversion results of the foundation lightning detection network in the real environment.
However, currently, for the problems of estimation of the positioning accuracy of the detection network and optimization of false signals and data with large deviation, only common methods such as monte carlo are used for estimation, and no better optimization method is available for actual observed data.
However, in reality, the land features are different in different regions, and the land flashback electromagnetic pulse finally reaches the observation substation with a certain hysteresis due to the influence of the mountain on the propagation of the land flashback electromagnetic pulse signal.
Disclosure of Invention
The application provides an optimization method of ground flashback positioning data considering path extension factors, and aims to solve the problem that the traditional method cannot optimize the ground flashback positioning data well.
The technical scheme adopted by the application for solving the technical problems is as follows:
an optimization method of ground flashback positioning data considering path elongation factors, comprising the following steps:
determining the level of the influence of different characteristic parameters of the observation substation on the ground flash-back positioning precision;
gridding the positioning precision distribution of the observation substations in the detection area according to the grade;
determining positioning errors of different grids according to propagation time delay of ground flashback electromagnetic fields caused by complex terrains in the detection net area;
according to different requirements for ground flashback positioning errors, lightning data in the grids with the positioning errors larger than preset error requirements are removed;
and determining the difference between the lightning location data before and after optimization according to preset comparison data, and evaluating the quality control effect of the lightning location data.
Further, the gridding processing of the positioning accuracy distribution of the observation substations in the detection area according to the grade comprises the following steps:
determining the detection area according to a preset range;
carrying out gridding treatment on the detection area according to the preset grid side length;
the lightning radiation source point is arranged in the center of each grid.
Further, determining the positioning error of the different meshes comprises the steps of:
simulating and calculating the time of the lightning electromagnetic pulse of the lightning radiation source point reaching each observation substation, and determining the time error of the lightning electromagnetic pulse reaching each observation substation;
calculating the simulation position of the lightning radiation source point according to the time error;
determining a plane error of a simulated position of the lightning radiation source point and a real position of the lightning radiation source point;
a root mean square positioning error for each of the grids is determined.
Further, determining the positioning error of the different meshes further comprises the steps of:
determining the ideal propagation time of the lightning electromagnetic pulse of the lightning radiation source point, which propagates through the flat earth surface at the light speed to reach each observation substation;
determining the equivalent propagation time of the lightning electromagnetic pulse of the lightning radiation source point to each observation substation in an equivalent path of a real terrain;
setting a difference between the equivalent propagation time and the ideal propagation time as a compensation time.
Further, the plane error includes a horizontal direction error and a vertical direction error, wherein,
DV＝|Zs-Zt|
in the formula, DHIndicating a horizontal error, DVDenotes the error in the vertical direction, XsX-coordinate value, Y-coordinate value representing three-dimensional space coordinate of analog positioned radiation sourcesY-coordinate value, Z, of a radiation source representing simulated positioning in three-dimensional space coordinatessZ coordinate value, X coordinate value, in three-dimensional space coordinate of radiation source for representing analog positioningtX-coordinate value, Y-coordinate value representing real radiation source in three-dimensional space coordinatetY-coordinate value, Z, representing the real radiation source in three-dimensional space coordinatestAnd the Z coordinate value of the real radiation source in the three-dimensional space coordinate is represented.
Further, the time for the lightning electromagnetic pulse to propagate to the observation substation is the sum of the ideal propagation time and the compensation time.
Further, the characteristic parameters include the longitude and latitude of the observation substations, the number of the observation substations, and the inherent time deviation of the GPS of the observation substations.
Further, the grade of the influence of the observation substation on the ground flashback positioning accuracy caused by different characteristic parameters of the observation substation is determined by utilizing a Monte Carlo algorithm.
The technical scheme provided by the application comprises the following beneficial technical effects:
the optimization method of the ground flashback location data considering the path extension factors comprises the steps of firstly determining the level of the influence of different characteristic parameters of an observation substation on the ground flashback location precision; then, gridding the positioning precision distribution of the observation substations in the detection area according to the determined different grades; determining positioning errors of different grids according to propagation time delay of ground flashback electromagnetic fields caused by complex terrains in the detection net area; then, according to different requirements on ground flash back positioning errors, removing lightning data in grids with positioning errors larger than preset error requirements; and finally, determining the difference between the lightning positioning data before and after optimization according to preset comparison data, and evaluating the quality control effect of the lightning positioning data. The optimization method for the ground flashback location data provided by the application is characterized in that the additional time deviation brought by a terrain propagation path is added on the inherent time deviation of observation substation hardware, and the Monte Carlo algorithm is utilized to realize real-time optimization and processing of the location deviation problem of the observation substation.
Drawings
FIG. 1 is a flowchart illustrating the main steps of a method for optimizing location data of a ground flashback system in consideration of a path extension factor according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a specific implementation step of an optimization method for location data of a ground flashback system, which considers a path extension factor according to an embodiment of the present application;
fig. 3 is an equivalent schematic diagram of a path extension for a real terrain according to an embodiment of the present application.
Detailed Description
For the purpose of describing and understanding the technical solutions of the present application, the technical solutions of the present application will be further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, an embodiment of the present application provides an optimization method for ground flashback positioning data considering path elongation factors. The method mainly comprises the following implementation steps: firstly, analyzing the positioning precision distribution of the observation substation by utilizing a Monte Carlo algorithm according to the layout of the existing observation substation, the inherent time deviation of a GPS of the observation substation and the delay of the arrival of ground flash-back time caused by terrain; then according to different requirements of different industries and different departments on ground flash-back positioning accuracy, eliminating the lightning data of the area with large error obtained by the Monte Carlo algorithm analysis, and achieving the effect of data quality control; and finally, comparing and analyzing the difference between the lightning positioning data before and after optimization by using radar combined reflectivity or Cloud Top Temperature (CTT) data of the wind-four satellite, and evaluating the effect of data quality control.
Specifically, referring to fig. 2, the specific implementation steps of the method for optimizing location data of a ground flashback click considering a path elongation factor according to the embodiment of the present application are as follows: firstly, analyzing the influence of the inconsistency of the arrangement of the observation substations, the number of the observation substations and the GPS time of the observation substations on the ground flashback positioning precision by utilizing a Monte Carlo algorithm, and carrying out lattice treatment on the positioning precision distribution of the observation substations in a detection area; then, analyzing the influence of ground flashback electromagnetic field propagation time delay caused by complex terrain in the detection network area on lattice positioning accuracy by utilizing a Monte Carlo algorithm, and determining positioning deviation of different lattice points; according to different requirements of different industries and different departments on ground flash-back positioning accuracy, the lightning data of the lattice point area with large error obtained by Monte Carlo algorithm analysis is removed, and the effect of data quality control is achieved; and comparing and analyzing the difference between the lightning positioning data before and after optimization by using the radar combined reflectivity or cloud top brightness temperature data of the wind-four satellite, and evaluating the effect of data quality control.
The following describes, by taking a specific embodiment as an example, a method for optimizing ground backswing positioning data in consideration of a path extension factor according to an embodiment of the present application.
When the Monte Carlo algorithm is used for simulating and analyzing the positioning accuracy of the observation substation, the method mainly comprises the following steps:
and S1, gridding the detection area. The size of the simulation analysis area range is 200km multiplied by 200km, and the center of the simulation analysis area is the geometric center of the positions of 7 observation substations; and gridding the simulation analysis area space, wherein the position of a lightning radiation source point is arranged at the center of each grid, the size of the grid is 5km multiplied by 5km, and the radiation sources are respectively at the heights of 0km, 1km, 5km and 10 km.
And S2, simulating and calculating the time of the radiation source point reaching each observation substation, and superposing time errors. When the arrival time is calculated, the influence of factors such as complex terrain on the time measurement is ignored, and the radiation source pulse is supposed to be transmitted linearly at the light speed; random time measurement errors with the mean value of 0 mu s and standard deviations SD of 100ns, 200ns and 300ns are selected to simulate the arrival time errors of real lightning electromagnetic pulses.
S3, calculating the position of the radiation source after the superposition time measurement error, and calculating the plane error D between the positions of the simulated positioning radiation source and the real radiation sourceHAnd DV. The calculation method comprises the following steps:
DV＝|Zs-Zt|
in the formula, DHIndicating a horizontal error, DVDenotes the error in the vertical direction, XsX-coordinate value, Y-coordinate value representing three-dimensional space coordinate of analog positioned radiation sourcesY-coordinate value, Z, of a radiation source representing simulated positioning in three-dimensional space coordinatessZ coordinate value, X coordinate value, in three-dimensional space coordinate of radiation source for representing analog positioningtX-coordinate value, Y-coordinate value representing real radiation source in three-dimensional space coordinatetY-coordinate value, Z, representing the real radiation source in three-dimensional space coordinatestAnd the Z coordinate value of the real radiation source in the three-dimensional space coordinate is represented.
And S4, repeating the steps S1-S3100 times to obtain 100 positioning errors of the corresponding grids so as to obtain the root mean square positioning error on each grid.
Time compensation for the effects of terrain delay is considered.
Neglecting the error of the GPS timing precision of the observation substation, the time t of the signal propagating from the lightning stroke point to the observation substation changes due to the influence of terrain, conductivity and the like, and the propagation time t of the signal is equal to the propagation time t of the ideal flat earth surface with the same horizontal propagation distanceflatWith a certain deviation, i.e.
t＝tflat+Δt
In the formula, tflatΔ t is the time for the signal to travel through a flat surface at the speed of light, and is the deviation in travel time between the actual rough terrain condition and the ideal flat surface condition.
Referring to fig. 1, a schematic diagram of an equivalent path for time compensation is shown in fig. 1. The curve of the rugged terrain in the graph is a terrain profile from a lightning strike positioning point to an observation substation, a dotted line similar to the envelope is the maximum envelope of the terrain obtained through a convex hull function, and a straight line of a straight line path is a straight line connecting a lightning point and an observation point.
When time compensation is carried out, the time difference of the signal propagating along the dotted line of the envelope approximation and the straight line of the straight line path at the speed of light is used as the estimation of delta t in the formula so as to reduce the propagation time t and t under the ideal conditionflatThe deviation of (2).
Through the steps, the extra time deviation brought by the terrain propagation path is added to the inherent time deviation of the observation substation hardware. And then, the positioning deviation problem of the observation substation can be optimized and processed in real time by utilizing a Monte Carlo algorithm.
Claims (8)
1. An optimization method of ground flashback positioning data considering path elongation factors, characterized by comprising the following steps:
determining the level of the influence of different characteristic parameters of the observation substation on the ground flash-back positioning precision;
gridding the positioning precision distribution of the observation substations in the detection area according to the grade;
determining positioning errors of different grids according to propagation time delay of ground flashback electromagnetic fields caused by complex terrains in the detection net area;
according to different requirements for ground flashback positioning errors, lightning data in the grids with the positioning errors larger than preset error requirements are removed;
and determining the difference between the lightning location data before and after optimization according to preset comparison data, and evaluating the quality control effect of the lightning location data.
2. The method for optimizing ground flashback positioning data considering path elongation factors according to claim 1, wherein gridding the positioning accuracy distribution of the observation substations in the detection area according to the grades comprises the following steps:
determining the detection area according to a preset range;
carrying out gridding treatment on the detection area according to the preset grid side length;
the lightning radiation source point is arranged in the center of each grid.
3. The method for optimizing ground flashback positioning data considering path elongation factors according to claim 2, wherein determining positioning errors for different grids comprises the steps of:
simulating and calculating the time of the lightning electromagnetic pulse of the lightning radiation source point reaching each observation substation, and determining the time error of the lightning electromagnetic pulse reaching each observation substation;
calculating the simulation position of the lightning radiation source point according to the time error;
determining a plane error of a simulated position of the lightning radiation source point and a real position of the lightning radiation source point;
a root mean square positioning error for each of the grids is determined.
4. The method for optimizing location data on a ground flashback considering path elongation factors according to claim 3, wherein determining location errors for different grids further comprises the steps of:
determining the ideal propagation time of the lightning electromagnetic pulse of the lightning radiation source point, which propagates through the flat earth surface at the light speed to reach each observation substation;
determining the equivalent propagation time of the lightning electromagnetic pulse of the lightning radiation source point to each observation substation in an equivalent path of a real terrain;
setting a difference between the equivalent propagation time and the ideal propagation time as a compensation time.
5. The method for optimizing ground flashback positioning data considering path elongation factors, according to claim 3, wherein the plane errors include horizontal direction errors and vertical direction errors, wherein,
DV＝|Zs-Zt|
in the formula, DHIndicating a horizontal error, DVDenotes the error in the vertical direction, XsX-coordinate value, Y-coordinate value representing three-dimensional space coordinate of analog positioned radiation sourcesY-coordinate value, Z, of a radiation source representing simulated positioning in three-dimensional space coordinatessZ coordinate value, X coordinate value, in three-dimensional space coordinate of radiation source for representing analog positioningtX-coordinate value, Y-coordinate value representing real radiation source in three-dimensional space coordinatetY-coordinate value, Z, representing the real radiation source in three-dimensional space coordinatestAnd the Z coordinate value of the real radiation source in the three-dimensional space coordinate is represented.
6. The method for optimizing ground flashback positioning data, taking into account path-lengthening factors, of claim 4, wherein the time for the lightning electromagnetic pulse to propagate to the observation substation is the sum of the ideal propagation time and the compensation time.
7. The method for optimizing ground flashback positioning data, taking into account path-lengthening factors, of claim 1, wherein the characteristic parameters include the latitude and longitude of the observation substations, the number of the observation substations, the time bias inherent to the observation substations GPS.
8. The method for optimizing ground flashback positioning data considering path elongation factors, according to claim 1, wherein the determining of the level of impact of different characteristic parameters of observation substations on the ground flashback positioning accuracy of the observation substations is determined by using a Monte Carlo algorithm.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116430127A (en) * | 2023-06-14 | 2023-07-14 | 云南电力试验研究院(集团)有限公司 | Method for reducing lightning positioning ground flash error |
| CN116449117A (en) * | 2023-06-16 | 2023-07-18 | 云南电力试验研究院(集团)有限公司 | Three-dimensional lightning positioning method suitable for complex terrain |
| CN117473878A (en) * | 2023-12-27 | 2024-01-30 | 青岛市生态与农业气象中心(青岛市气候变化中心) | Ground flash intensity inversion method based on stationary satellite data |
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- 2021-09-23 CN CN202111116152.4A patent/CN113850908A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116430127A (en) * | 2023-06-14 | 2023-07-14 | 云南电力试验研究院(集团)有限公司 | Method for reducing lightning positioning ground flash error |
| CN116430127B (en) * | 2023-06-14 | 2023-10-20 | 云南电力试验研究院(集团)有限公司 | Method for reducing lightning positioning ground flash error |
| CN116449117A (en) * | 2023-06-16 | 2023-07-18 | 云南电力试验研究院(集团)有限公司 | Three-dimensional lightning positioning method suitable for complex terrain |
| CN116449117B (en) * | 2023-06-16 | 2023-08-15 | 云南电力试验研究院(集团)有限公司 | Three-dimensional lightning positioning method suitable for complex terrain |
| CN117473878A (en) * | 2023-12-27 | 2024-01-30 | 青岛市生态与农业气象中心(青岛市气候变化中心) | Ground flash intensity inversion method based on stationary satellite data |
| CN117473878B (en) * | 2023-12-27 | 2024-03-15 | 青岛市生态与农业气象中心(青岛市气候变化中心) | Ground flash intensity inversion method based on stationary satellite data |
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