CN109324335B - Method and system for identifying wind shear based on laser radar - Google Patents
Method and system for identifying wind shear based on laser radar Download PDFInfo
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- CN109324335B CN109324335B CN201811542782.6A CN201811542782A CN109324335B CN 109324335 B CN109324335 B CN 109324335B CN 201811542782 A CN201811542782 A CN 201811542782A CN 109324335 B CN109324335 B CN 109324335B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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Abstract
The invention relates to a method and a system for identifying wind shear based on a laser radar, wherein the method comprises the following steps: acquiring radial wind field detection data on a glide slope and around an airport runway, which are acquired by a laser radar, in real time; processing radial wind field detection data to obtain a glidepath wind profile; wind shear information of the glidepath wind profile is identified by wavelet transformation. According to the embodiment of the invention, the radial wind field detection data on the glide slope and the periphery of the airport runway, which are acquired by the laser radar, are acquired in real time, and the wavelet transformation is adopted to conduct wind shear recognition, so that the accuracy is high, and the calculation cost is greatly reduced.
Description
Technical Field
The invention relates to the technical field of atmospheric science, in particular to a method and a system for identifying wind shear based on a laser radar.
Background
Currently, most countries such as the united states mainly use a low-altitude wind shear early warning system (Low Level Wind shear Alert System, LLWAS) based on ground wind sensors to detect and early warn the wind shear in airport areas. Because LLWAS adopts a ground sensor as a main part, the detection capability of the LLWAS on a high-altitude wind field is weak, and the LLWAS lacks of authenticity and data accuracy. Unlike the wind shear caused by larger scale weather systems in the united states, airports in certain areas of our country suffer from rapidly varying small scale terrain winds, and the use of LLWAS systems does not effectively solve the wind shear identification problem that plagues airports in our country.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method and a system for identifying wind shear based on a laser radar aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows: a method of identifying wind shear based on lidar, comprising:
radial wind field detection data on a glide slope and around an airport runway, which are acquired by laser radars, are acquired in real time, wherein the laser radars are deployed in the middle section of the airport runway, and the elevation angle of the laser radars is the same as the gradient of the glide slope;
processing the radial wind field detection data to obtain a glidepath wind profile;
wind shear information of the glidepath wind profile is identified by wavelet transformation.
The beneficial effects of the invention are as follows: the radial wind field detection data on the glide slope and the periphery of the airport runway are acquired in real time, and the wavelet transformation is adopted for wind shear recognition, so that the accuracy is high, and the calculation cost is greatly reduced. The method is different from the existing wind shear recognition method, and is mainly based on the main change trend of radial wind.
On the basis of the technical scheme, the invention can be improved as follows.
Further, before processing the radial wind field detection data to obtain a glidepath wind profile, the method further includes:
and performing quality control on radial wind field detection data on each range gate to remove unreliable data.
Further, the quality control of the radial wind field detection data on each range gate to remove the unreliable information includes:
when the missing rate of the radial wind field detection data on the same range gate exceeds a preset ratio, the data of the range gate and the farther range gate are determined to be unreliable and removed.
The beneficial effects of adopting the further scheme are as follows: because the laser radar has a certain detection range, the performance is objectively influenced by weather and atmospheric pollution conditions, and the uncertainty is high. The more the distance is, the weaker the return signal is, so that the laser radar is difficult to identify, and therefore, more lack points exist, at the moment, the points with measurement data are also often interfered by noise and are not trusted, so that the actual detection range of the laser radar can be dynamically identified by judging whether the lack rate of the data on the same distance door exceeds a preset ratio, and the unreliable data can be effectively removed.
Further, the processing the radial wind field detection data to obtain a glidepath wind profile includes:
interpolation is carried out on the beam radial wind speed and wind direction observation results, which are positioned near the glide slope, in the radial wind field detection data to obtain the radial wind speed and wind direction information of the glide slope;
inversion processing is carried out on the radial wind field detection data by using a VAD method, so that crosswind information of the glidepath is obtained;
and obtaining the air profile of the glidepath according to the radial air speed and direction information of the glidepath and the crosswind information of the glidepath.
The beneficial effects of adopting the further scheme are as follows: the head wind/the tail wind of the glide slope is directly observed by utilizing radial wind, so that a complex inversion algorithm and introduced errors are avoided, and the calculation speed is greatly improved; meanwhile, the crosswind of the glide slope is inverted by combining the VAD method, so that the defect that the radial wind algorithm cannot observe the crosswind is overcome.
Further, if the wind shear information includes a wind shear region, the identifying, by wavelet transformation, wind shear information of the glidepath wind profile includes:
and identifying main characteristic points of the glidepath wind profile by adopting Haar wavelet transformation, and taking the main characteristic points of the glidepath wind profile as boundaries of the wind shear area.
The beneficial effects of adopting the further scheme are as follows: and wind shear recognition is performed by wavelet transformation, so that the recognition accuracy is improved, and the calculation cost is reduced.
Further, the wind shear information further includes a wind shear strength, the method further comprising:
and determining an area for issuing an alarm and alarm information corresponding to the area according to the wind shear strength and the wind shear strength standard of the ICAO.
The beneficial effects of adopting the further scheme are as follows: wind shear can be timely found and an alarm can be issued, so that loss is reduced.
Further, the determining, according to the wind shear strength and the wind shear strength standard of the ICAO, an area for issuing an alarm and alarm information corresponding to the area includes:
judging the wind shear grade in each area on the glidepath according to the wind shear strength standard of the ICAO and the wind shear strength, wherein each area is obtained by dividing the glidepath by 1 sea in units;
identifying an area of which the wind shear level is above the middle level;
and issuing a single regional alarm for each identified region, wherein the alarm information corresponding to each region comprises a stroke shear maximum intensity value in the region.
The beneficial effects of adopting the further scheme are as follows: the method provides concise and effective wind shear information for pilots, and is convenient for the pilots to deal with in time.
The other technical scheme for solving the technical problems is as follows: an apparatus for identifying wind shear based on lidar, comprising:
a memory for storing a computer program;
and the processor is used for executing the computer program to realize the method for identifying wind shear based on the laser radar according to the technical scheme.
The other technical scheme for solving the technical problems is as follows: a system for identifying wind shear based on lidar, comprising:
the laser radar is deployed in the middle section of the airport runway, and the elevation angle of the laser radar is the same as the gradient of the glide slope and is used for acquiring radial wind field detection data on the glide slope and around the airport runway;
according to the technical scheme, the device for identifying wind shear based on the laser radar is used for processing radial wind field detection data acquired by the laser radar and identifying wind shear information on a glide slope.
The beneficial effects of the invention are as follows: radial wind field detection data on a glide slope and around an airport runway, which are acquired by a laser radar, are acquired in real time, and wind shear identification is performed by wavelet transformation, so that not only is the accuracy high, but also the calculation cost is greatly reduced.
The other technical scheme for solving the technical problems is as follows: a storage medium having instructions stored therein, which when read by a computer, cause the computer to perform a method of identifying wind shear based on lidar as described in the above-mentioned aspects.
Additional aspects of the invention and advantages thereof will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the embodiments of the present invention or the drawings used in the description of the prior art, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for identifying wind shear based on a lidar according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for identifying wind shear based on lidar according to another embodiment of the present invention;
FIG. 3 is a flowchart of a method for identifying wind shear based on lidar according to another embodiment of the present invention;
FIG. 4 is a flowchart of a method for identifying wind shear based on lidar according to another embodiment of the present invention;
FIG. 5 is a flowchart of a method for identifying wind shear based on lidar according to another embodiment of the present invention;
fig. 6 is a schematic diagram of a primary wind shear recognition result of a method for recognizing wind shear based on a lidar according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
A method 100 of identifying wind shear based on lidar as shown in fig. 1, comprising:
110. radial wind field detection data on a glide slope and around an airport runway, which are acquired by a laser radar, are acquired in real time. The laser radar is deployed in the middle section of the airport runway, and the elevation angle of the laser radar is the same as the gradient of the glide slope.
120. And processing the radial wind field detection data to obtain a glidepath wind profile.
130. Wind shear information of the glidepath wind profile is identified by wavelet transformation.
Specifically, in this embodiment, the lidar is deployed near the middle section of the airport runway, and periodically scans the periphery or 360 ° PPI, for example, using the same 3 ° elevation angle as the glide slope: a set of PPI scans was completed every 2 minutes, resulting in PPI scan data. The PPI scan data includes: radial wind speed and direction information on the glidepath and radial wind field information around the airport runway.
The method for identifying wind shear based on the laser radar provided in the above embodiment comprises the following steps: radial wind field detection data on a glide slope and around an airport runway, which are acquired by a laser radar, are acquired in real time, and wind shear identification is performed by wavelet transformation, so that not only is the accuracy high, but also the calculation cost is greatly reduced.
It should be appreciated that in this embodiment, the wind shear information includes a wind shear region, and step 140 may specifically be: and identifying main characteristic points of the glidepath wind profile by adopting Haar wavelet transformation, and taking the main characteristic points of the glidepath wind profile as boundaries of a wind shear area.
Specifically, haar wavelet transforms are employed to identify the principal feature points of the wind profile. The wavelet transform computes the accumulated value W of the product at each height point by constructing a wavelet function h, the profile and the function ranging from a lower height limit zb to an upper height limit zt.
The actual calculation of W is a gradient of an average value of the portions of the upper and lower a/2 widths centered on the point of the height b, so that the extremum of the signal gradient appears as the peak (valley) of the wavelet signal. By reasonably adjusting the width a, the wavelet transformation can effectively filter out high-frequency and low-frequency fluctuation of the signal, so that only the fluctuation part of a specific wavelength interval with the half width of the wave crest nearby the a is reserved.
The main characteristic points of the glidepath wind profile are the main wave crest/wave trough positions in the whole profile, and the wave crest and wave trough with small fluctuation and the wave crest and wave trough with relatively minor wave crest and wave trough are eliminated.
Since the main characteristic points are the peaks and the troughs of the main fluctuation of the head-tail wind profile of the glide slope, and the two adjacent main characteristic points are necessarily the peaks and the troughs respectively, the wind speed change between the two adjacent characteristic points can be regarded as monotonous change on the whole, in other words, the positive and negative properties of the wind shear between the two points are regarded as consistent. Therefore, wind shear strength can be obtained by calculating the wind speed difference of adjacent feature points and dividing the wind speed difference by the distance.
Through wavelet transformation, main characteristic points of the glidepath wind profile are identified and then serve as boundaries of a wind shear region.
Specifically, in this embodiment, the main peak and trough of the wind speed profile is obtained by three rounds of recognition algorithm based on the wavelet transform method.
1. Wave crest and wave trough identification algorithm
Through wavelet transformation, the wave crest and the wave trough of the signal are close to 0 value in the wavelet signal, and the extreme value position of the signal transformation rate is represented as the wave crest and the wave trough in the wavelet signal. The position of the signal peaks and troughs can thus be determined.
2. Secondary point filtering algorithm
For the peak and trough identified in 1, there are the following problems:
(1) Small fluctuation still exists in the stable state of the wind field, and the amplitude of the wave crest and the wave trough is too small at the moment, so that the wind field is unimportant in wind speed fluctuation.
(2) Some small fluctuation may exist in one large wind speed fluctuation, and the recognition result recognizes the wave crests and wave troughs of the small fluctuation as characteristic points, so that large fluctuation is cracked, and the strength is greatly weakened.
Thus, a secondary point filtering algorithm needs to be added to highlight the primary wind speed fluctuation information.
The filtering algorithm uses the recognition result in the step 1 to form a profile for carrying out peak-valley recognition again, carries out paired removal on secondary points and highlights the positions of main characteristic points. The problem of (2) in 2 is better solved.
3. Amplitude filtering algorithm
In order to further solve the problem in (1) in 2, an amplitude filtering algorithm is further added, and two adjacent feature points with the amplitude lower than a certain threshold value are removed from the feature point sequence at the same time.
As shown in fig. 6, the primary wind shear recognition result is shown. The abscissa is the aircraft height, the ordinate is the wind speed, the solid curve is the head-tail wind, and the head wind is positive; the dotted curve is the crosswind, with the left side wind coming positive.
The glide slope is 3 degrees so that the distance to the ground can be calculated from the height, and is divided into 3 whole sea areas in the figure. Within the thin line box is an entire sea area (2 Nmile).
Through wavelet transformation algorithm, main characteristic points (peak valley) on the head-tail wind profile (solid curve) can be identified, namely, the solid small circles in the graph. And calculating the wind speed gradient of the region surrounded by the two adjacent characteristic points, namely the wind shear strength. When the intensity exceeds the standard, the region is the wind shear region (thick line box).
Taking this figure as an example, it can be seen that there is a wind shear region of intensity-4.3 spanning two alert regions of 2 and 3 seas, where the alert is: 3 sea-liner, wind shear-4.3; 2 sea ry wind shear-4.3.
Optionally, as an embodiment, as shown in fig. 2, before step 120, the method 100 further includes:
140. and performing quality control on radial wind field detection data on each range gate to remove unreliable data.
Then, step 120 specifically includes: and processing the radial wind field detection data from which the unreliable data are removed to obtain a glidepath wind profile.
Specifically, in this embodiment, when the rate of absence of the radial wind field detection data on the same range gate exceeds a preset ratio, it is determined that the data of the range gate and the farther range gate are not authentic and removed.
For example, if the rate of loss of radial wind field detection data on the same range gate exceeds 50% or 60%, or other ratios are possible, the rate of loss of data on the range gate is too high to be trusted, and the data on the range gate and the range gate farther than the range gate need to be removed. For example: if the door deletion rate is too high at a distance of 2500 m, all data from 2500 m to longer distances are considered unreliable.
Alternatively, in another embodiment, as shown in fig. 3, step 120 may include:
121. and interpolating the beam radial wind speed and direction observation result positioned near the glide slope in the radial wind field detection data to obtain the radial wind speed and direction information of the glide slope.
122. And carrying out inversion processing on the radial wind field detection data by using the VAD method to obtain the crosswind information of the glidepath.
123. And obtaining the air profile of the glidepath according to the radial air speed and direction information of the glidepath and the crosswind information of the glidepath.
Specifically, in this embodiment, the glidepath wind profile includes: head/tail wind and side wind, wherein the head/tail wind is divided into head wind and tail wind, which respectively refer to wind blown from the head/tail. The radial wind algorithm can obtain the head and tail wind information directly, but the VAD algorithm is needed to assist in obtaining the crosswind.
When the wind profile of the glidepath is calculated, the wind speed and the wind direction have corresponding position information, and the wind speed and the wind direction values on the glidepath are obtained by interpolation according to the wind speed and the wind direction distribution near the glidepath. The method has the defect that the crosswind information of the glidepath cannot be obtained, so that the VAD method is used for processing PPI scanning data, the obtained wind vector is decomposed along the glidepath, and the crosswind component is used as the crosswind information of the glidepath.
The VAD method is a common method for inverting wind fields by using a laser radar at present. The method is to assume uniform wind field at the same height, set wind speed as V (H), wind direction as theta (H), and know radial velocity V for beam with azimuth angle alpha r The method comprises the following steps:
V r ＝V(H)*sin(α-θ(H))
thus, in 360 ° PPI scan data, the radial velocity on the same range gate should exhibit a sinusoidal morphology. Fitting is performed through a sine function, and then the amplitude V (H) and the phase theta (H) can be obtained.
The VAD method can only obtain unique wind speed and direction results for each height layer, and can be used for providing side wind components of a glidepath wind field in the embodiment of the invention.
In the embodiment, the radial wind is utilized to directly observe the head/tail wind of the glide slope, so that a complex inversion algorithm and introduced errors are avoided, and the calculation speed is greatly improved; meanwhile, the crosswind of the glide slope is inverted by combining the VAD method, so that the defect that the radial wind algorithm cannot observe the crosswind is overcome.
It should be appreciated that in this example, step 140 may also be included prior to step 121. Then step 121 is specifically: and in the radial wind field detection data from which the unreliable data is removed, the radial wind speed and the wind direction of the wave beam on the glide slope are interpolated on the glide slope to be used as the radial wind speed and the wind direction information of the glide slope. The step 12 is specifically as follows: and carrying out inversion processing on the radial wind field detection data from which the unreliable data are removed by using a VAD method to obtain the crosswind information of the glidepath.
Optionally, in another embodiment, as shown in fig. 4, the method 100 may further include:
150. and determining an area for issuing an alarm and alarm information corresponding to the area according to the wind shear strength and the wind shear strength standard of the international civil aviation organization ICAO (International Civil Aviation Organization).
Specifically, in this embodiment, as shown in fig. 5, step 150 may include:
151. judging wind shear grades in all areas on the glide slope according to the wind shear strength standard and the wind shear strength of ICAO, wherein all areas are obtained by dividing the glide slope by 1 sea area;
152. identifying an area of which the wind shear level is above the middle level;
153. and issuing a single regional alarm for each identified region, wherein the alarm information corresponding to each region comprises a stroke shear maximum intensity value in the region.
Specifically, in this embodiment, the alarm information may also include a wind shear type or the like. Wind shear types include wind shear (wind), turbulence (turbulance), and downburst (downburst), where downburst requires synchronous observation of airport Doppler weather radar for identification. In addition, the wind shear alarm can be stored and recorded according to the wind shear time.
The pilot needs to obtain the wind shear strength and position information when landing, but the aircraft is only in 1-2 minutes during landing, and the pilot cannot process all the wind shear identification information. It is therefore internationally common practice to divide the glidepath into several areas, giving only the strongest wind shear in each area as an alert for that area, so that the pilot only has to deal with several area alerts and can meet his needs for wind shear location.
For example, a glidepath is divided into 3 areas in 1 sea, and a single area alarm is issued for each area, for which the maximum intensity value of stroke shear is measured. Each whole sea area refers to a whole sea glidepath area. The wind shear region is the region between the two adjacent feature points mentioned above. For example, when a wind shear region starts and ends (1.2, 2.3) in the sea, the region spans two entire sea regions. While an entire sea area may contain multiple wind shear areas or portions thereof, only a single area alarm is issued for that entire sea area using the highest wind shear strength. For example, there may be four wind shear zones of level 2, level 3, level 2 within the 1 sea area, at which time an alarm is issued as level 3 wind shear for the 1 sea area.
According to the method for identifying wind shear based on the laser radar, provided by the embodiment, the laser wind-finding radar is adopted to continuously detect wind fields around the aircraft glide slope and the airport, so that wind field information near a target area can be detected in real time under higher space-time resolution, and the wind shear can be found in time and an alarm can be issued.
It should be understood that, in the above embodiment, step 140 may be further included, or step 120 may be specifically steps 121-123, which are not repeated herein for brevity of description.
It should be further understood that, in the foregoing embodiments of the present invention, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
The method for identifying wind shear based on the lidar provided by the embodiment of the invention is described in detail above with reference to fig. 1 to 5, and the system for identifying wind shear based on the lidar provided by the embodiment of the invention is described in detail below.
The invention also provides a device for identifying wind shear based on the laser radar, which comprises:
a memory for storing a computer program;
a processor configured to execute a computer program to implement a method of identifying wind shear based on lidar as in any of the embodiments above.
It should be noted that, this embodiment is a product embodiment corresponding to the foregoing method embodiments, and specific functions and descriptions of optional implementations of each structure in this embodiment may refer to corresponding descriptions in the foregoing method embodiments, which are not repeated herein.
In addition, the invention also provides a system for identifying wind shear based on the laser radar, which comprises: lidar and a device for identifying wind shear based on lidar as in the above embodiments. Wherein,,
the laser radar is deployed in the middle section of the airport runway, and the elevation angle of the laser radar is the same as the gradient of the glide slope, and is used for periodically scanning or 360-degree PPI scanning to acquire radial wind field detection data on the glide slope and around the airport runway. The device is used for processing radial wind field detection data acquired by the laser radar and identifying wind shear information on the glide slope.
For example, a lidar may complete a set of PPI scans every 2 minutes.
It should be noted that, the present embodiment is a system embodiment corresponding to the above-mentioned apparatus embodiment, and the specific function and the description of the optional implementation manner of the apparatus for identifying wind shear based on lidar in the present embodiment may refer to the corresponding description in the above-mentioned method embodiments, which are not repeated herein.
The system for identifying wind shear based on the lidar provided in the above embodiment: radial wind field detection data on a glide slope and around an airport runway, which are acquired by a laser radar, are acquired in real time, and wind shear identification is performed by wavelet transformation, so that not only is the accuracy high, but also the calculation cost is greatly reduced.
In other embodiments of the present invention, there is also provided a storage medium having stored therein instructions which, when read by a computer, cause the computer to perform the method of identifying wind shear based on lidar as in any of the embodiments described above.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. A method of identifying wind shear based on lidar, comprising:
radial wind field detection data on a glide slope and around an airport runway, which are acquired by laser radars, are acquired in real time, wherein the laser radars are deployed in the middle section of the airport runway, and the elevation angle of the laser radars is the same as the gradient of the glide slope;
processing the radial wind field detection data to obtain a glidepath wind profile;
identifying wind shear information of the glidepath wind profile by wavelet transformation;
the processing of the radial wind field detection data to obtain a glidepath wind profile includes:
interpolation is carried out on the beam radial wind speed and wind direction observation results, which are positioned near the glide slope, in the radial wind field detection data to obtain the radial wind speed and wind direction information of the glide slope;
inversion processing is carried out on the radial wind field detection data by using a VAD method, so that crosswind information of the glidepath is obtained;
obtaining a glidepath wind profile according to radial wind speed and wind direction information of the glidepath and side wind information of the glidepath;
the wind shear information includes a wind shear region, and the identifying the wind shear information of the glidepath wind profile by wavelet transformation includes:
identifying main characteristic points of the glidepath wind profile by adopting Haar wavelet transformation, and taking the main characteristic points of the glidepath wind profile as boundaries of the wind shear area;
the main characteristic points of the glidepath wind profile are main wave crest/wave trough positions in the whole profile, and the process for identifying the main characteristic points of the glidepath wind profile by adopting Haar wavelet transformation comprises the following steps:
based on the wavelet transformation method, main wave crest/wave trough positions are obtained through a three-wheel recognition algorithm, wherein the three-wheel recognition algorithm comprises: peak-trough recognition algorithms, secondary point filtering algorithms, and amplitude filtering algorithms.
2. The method of claim 1, wherein prior to processing the radial wind field detection data to obtain a glidepath wind profile, the method further comprises:
and performing quality control on radial wind field detection data on each range gate to remove unreliable data.
3. The method for identifying wind shear based on lidar of claim 2, wherein the quality control of the radial wind field detection data on each range gate to remove the un-trusted information comprises:
when the missing rate of the radial wind field detection data on the same range gate exceeds a preset ratio, the data of the range gate and the farther range gate are determined to be unreliable and removed.
4. A method of identifying wind shear based on lidar according to any of claims 1-3, wherein the wind shear information further comprises a wind shear strength, the method further comprising:
and determining an area for issuing an alarm and alarm information corresponding to the area according to the wind shear strength and the wind shear strength standard of the ICAO.
5. The method for identifying wind shear based on lidar according to claim 4, wherein determining an area for issuing an alarm and alarm information corresponding to the area according to the wind shear strength and a wind shear strength standard of ICAO comprises:
judging the wind shear grade in each area on the glidepath according to the wind shear strength standard of the ICAO and the wind shear strength, wherein each area is obtained by dividing the distance between the glidepath and the runway end by 1 sea;
identifying an area of which the wind shear level is above the middle level;
and issuing a single regional alarm for each identified region, wherein the alarm information corresponding to each region comprises a stroke shear maximum intensity value in the region.
6. A device for identifying wind shear based on a laser radar is characterized in that,
a memory for storing a computer program;
a processor for executing the computer program for implementing a method of identifying wind shear based on lidar according to any of claims 1 to 5.
7. A system for identifying wind shear based on lidar, comprising:
the laser radar is deployed in the middle section of the airport runway, and the elevation angle of the laser radar is the same as the gradient of the glide slope and is used for acquiring radial wind field detection data on the glide slope and around the airport runway;
the lidar-based wind shear identification device of claim 6, wherein the device is configured to process the radial wind field detection data obtained by the lidar to identify wind shear information on a glideslope.
8. A storage medium having instructions stored therein which, when read by a computer, cause the computer to perform the method of identifying wind shear based on lidar of any of claims 1 to 5.
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| CN110162927A (en) * | 2019-06-06 | 2019-08-23 | 国耀量子雷达科技有限公司 | Takeoff and landing method for early warning based on flight simulation platform and anemometry laser radar |
| CN110288856A (en) * | 2019-06-21 | 2019-09-27 | 中国民用航空总局第二研究所 | The Scheduled Flight monitoring system and method for fine forecast based on wind |
| CN111208534A (en) * | 2020-01-20 | 2020-05-29 | 安徽四创电子股份有限公司 | Method for joint detection and identification of wind shear by using laser radar and wind profile radar |
| CN111652435A (en) * | 2020-06-03 | 2020-09-11 | 上海眼控科技股份有限公司 | Wind shear prediction method, wind shear prediction device, computer equipment and readable storage medium |
| CN112965084B (en) * | 2021-01-28 | 2021-10-22 | 中国人民解放军国防科技大学 | Airport wind field characteristic detection method, device and equipment based on laser radar |
| CN115494521B (en) * | 2022-09-22 | 2023-08-22 | 中国民航大学 | Airport runway low-altitude wind shear early warning method based on laser radar |
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